Speech Perception Challenges: The Role of Second Language Experience Across Masker Types in native Malayalam speakers for Low-frequency PB words

Speech Perception Challenges: The Role of Second Language Experience Across Masker Types in native Malayalam speakers for Low-frequency PB words | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Speech Perception Challenges: The Role of Second Language Experience Across Masker Types in native Malayalam speakers for Low-frequency PB words PRADEEP KUMAR MAHAPATRA, DHANANJAY RACHANA, RITWIK PRAKSH, ASHISH BISHT This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7750783/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background Speech perception in noise is influenced by factors like the type of background noise, the language of the noise, and the listener’s linguistic experience. In multilingual contexts like India, understanding how second language learning affects native language speech processing is crucial. Such investigations remain largely unexplored within the Indian multilingual context. The study aimed to explore the effect of new language learning on speech perception with low-frequency native Malayalam phonetically balanced (PB) words in the presence of different masker conditions. Methods A cross-sectional study included 54 native Malayalam-speaking participants in two groups: Experienced (n = 27; >3 years Kannada exposure) and non-experienced (n = 27, no Kannada exposure) participant groups. SNR-50 and masking release were assessed using the Malayalam low-frequency PB words in the presence of 2-talker (2TB) and 4-talker babble (4TB) in Malayalam, Kannada, and speech-shaped noise (SSN). Results In both experienced and non-experienced participants, SNR-50 performance followed a 2TB > SSN > 4TB trend. Both groups performed better with native (Malayalam) maskers than non-native (Kannada) maskers. Overall, the experienced group outperformed the non-experienced group, though in all masking conditions, differences were not statistically significant. Interestingly, the non-experienced group showed more masking release with the 2TB and non-native (Kannada) masker. Conclusion Increasing the number of talker complexity (2TB to 4TB) showed decreased performance. Native-language maskers facilitated better speech perception, emphasizing the role of linguistic familiarity. Also, the experienced group demonstrated enhanced speech-noise segregation, improving target speech perception, particularly in complex masking conditions. In contrast, the non-experienced group benefits from reduced cognitive load in simpler auditory environments with non-native maskers. New Language experience Masking Release Malayalam Bilingualism Cross-linguistic interference Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 INTRODUCTION Speech recognition involves comprehending spoken sentences by integrating auditory and cognitive processes. Communication often occurs in adverse conditions where background noise significantly impacts speech intelligibility. In such environments, speech perception is influenced by several factors, including familiarity with linguistic and phonetic patterns, background noise characteristics, noise type, speaker and listener language, and target-to-noise ratio. In noisy settings, speech perception requires heightened attention, cognitive effort, and mental focus to decode the target signal amidst distractions. As a result, understanding speech is often more difficult in these conditions than in quiet environments ( 1 ). Competing sounds can mask important speech cues, distort phonetic details, and make it challenging to distinguish desired speech from the noisy environment. Navigating complex listening situations, like multi-talker babble, often requires discerning the target speech from a mix of competing voices. Speech perception becomes especially challenging when background sounds interfere with the listener’s ability to understand speech. These effects, known as masking, can be classified into two types: energetic and informational masking. Energetic masking occurs when competing maskers physically overlap with the target signal at the auditory periphery level, making it difficult for listeners to even detect the speech. Informational masking, on the other hand, happens at a more central level, where listeners struggle to separate the target speech from similar-sounding background elements, even when the signal is audible ( 2 ). Fortunately, listeners can sometimes overcome these challenges through a process known as masking release, which refers to the boost in perception that occurs when there are brief moments or "dips" in the speech babble as a masker, allowing the target speech to "peek through" the noise ( 3 ). A recent study by Li et al.( 4 ) suggests that masking release is even more pronounced when additional cues, like spatial separation or distinct voice characteristics, are available to help listeners focus on the target speech. In challenging environments, linguistic and phonetic overlap between the masker and target languages makes the speech perception even more challenging. Previous studies show that listeners struggle more to decode speech in noise, particularly those with limited familiarity with a masker language. Non-native listeners often have limited cues and less efficiently organized vocabulary in the second language, leading to slower and less accurate word recognition. In addition to this linguistic limitation, they must allocate more cognitive resources, such as working memory, attentional control, and processing speed, to comprehend speech. These resources are crucial for simultaneously decoding phonetic input, integrating syntactic structures, and accessing semantic meaning. In native listeners, much of this processing occurs automatically, but for non-native or bilingual individuals, it is often more effortful and resource-intensive, particularly under adverse listening conditions such as noise or competing speech. As a result, bilinguals may experience increased listening fatigue and reduced speech intelligibility in complex acoustic environments, even when they are proficient in both languages ( 5 ). Bilinguals can differ from monolinguals in terms of the allocation of cognitive resources and the processing of language, which will impact their discrimination between speech and noise ( 6 ). Although a number of studies have explored the effect of linguistic and phonetic similarities, along with listener familiarity, on speech recognition in noisy scenarios, the findings have not always been consistent. Researchers suggest that greater masking release occurs when both the target word and masker languages are familiar or native to the listener ( 7 , 8 ). Native listeners generally have an advantage in speech discrimination from background noise, likely due to stronger linguistic representations and more efficient processing ( 9 ). Also, a study ( 10 ) report significant differences between native and non-native listeners in consonant-vowel identification, highlighting that familiarity with language is not the sole predicting factor for speech perception. Von Hapsburg et al. ( 12 ) further emphasized that non-native language proficiency becomes especially important when the speech signal is degraded or presented in noisy background environments. Additionally, bilingual listeners may process speech differently from monolinguals due to the demands on their cognitive resources ( 13 ). Interestingly, speech recognition is superior if the target and masker languages are linguistically distinct, such as English versus Mandarin, as compared to if the languages are more similar to one another, such as English and Dutch ( 14 , 15 ). The above experiments suggest the contributions of language familiarity and linguistic distance towards impacting perception in noise for speech. Another significant factor affecting speech perception is the background talkers. Jain et al. ( 16 ) identified that native Malayalam listeners were better when the masker was their native tongue, Malayalam, as opposed to Kannada, and better when there were more talkers. This better performance was presumably because there was less informational masking with more talkers. Previous research highlighted the listener’s difficulty in complex environments, such as a study ( 17 ) reported that increasing the number of talkers makes speech perception more difficult, mainly because of greater energetic masking. Also, Hall et al., ( 18 ) and Brungart et al., ( 19 ) found that two-talker babble disrupts speech perception more than steady noise. Signal-to-noise ratio (SNR) also impacts speech perception; Jain et al., ( 16 ) found that speech recognition performance deteriorates at lower SNRs (e.g., -3 dB), whereas Houben et al., ( 20 ) observed improved recognition as SNR increased (e.g., + 4 dB). These results highlight the intricate relationship between linguistic, cognitive, and acoustic variables in speech perception with difficult listening conditions. India’s rich linguistic diversity creates unique listening environments that influence how individuals perceive speech. With 121 officially recognized mother tongues and six classical languages with deep historical roots ( 21 ), individuals are routinely exposed to both familiar and unfamiliar languages in daily communication. This rich yet complex linguistic backdrop presents challenges in measuring speech perception, especially when native and non-native maskers are involved. The present study focuses on Malayalam and Kannada due to their clinical and linguistic relevance in speech perception research. Malayalam, the participants’ native language, serves as the native masker, while Kannada—a widely spoken regional language in southern India—functions as the non-native masker. Even though both languages belong to the Dravidian family, they differ in phonetics, prosody, rhythm, and stress patterns. These aspects make them suitable to explore the influence of familiarity on speech perception under noise. Given the multilingual situation in India, most individuals receive active or passive exposure to Kannada owing to geographical closeness, migration, and schooling, making it apt to investigate non-native babble as well as the impact of masking release on speech perception. Additionally, audiologists of varying linguistic origins in cities such as Mangalore tend to test native and non-native listeners without standardized normative reference tasks for multi-source auditory testing environments ( 22 ). To obtain a more accurate measure of auditory processing, low-frequency PB word lists were employed to ensure that speech perception scores in normal-hearing individuals reflected true auditory processing abilities rather than the influence of lexical familiarity or contextual prediction. These types of words minimized top-down compensatory strategies, providing a more precise and controlled assessment of the listener's ability to process lower frequency acoustic information. Using low-frequency word lists in auditory speech perception research offers several important advantages, especially for language learners. Because these words are less familiar, they can reveal subtle difficulties in speech understanding that high-frequency words often miss. ( 23 ) They also require learners to focus more on the actual sounds, rather than relying on guesswork or prior knowledge, encouraging deeper engagement with the language ( 24 ). These words reflect the real-world challenge of encountering unfamiliar vocabulary ( 25 ), and their acoustic features can help learners better segment and understand speech in complex listening situation. This study investigates how linguistic experience affects speech intelligibility by examining how native Malayalam speakers interpret speech under various masking conditions. The study compares individuals with and without prior exposure to Kannada to assess how familiarity with a new language influences speech recognition in noise. The present study explores the effect of learning a new language on speech perception and masking release using low-frequency Malayalam PB words in different masker conditions. Exposure to Kannada is hypothesized to increase cognitive demands and cross-linguistic interference, making speech perception more challenging in both native (Malayalam) and non-native (Kannada) background noise. In contrast, individuals without Kannada exposure are expected to show better speech perception in the Kannada background due to reduced cross-language interference. The objectives of the study include measuring how a new language experience (exposure to Kannada) influences speech perception abilities (SNR-50 and MR) by comparing experienced and non-experienced Malayalam speakers across different types of noise maskers specifically two-talker babble (2TB), four-talker babble (4TB), speech-shaped noise (SSN) and between native (Malayalam) and non-native (Kannada) language masker conditions. METHODOLOGY The study utilized a 5x2 cross-sectional mixed design, integrating within-subject and between-subject factors. The within-subject factors encompassed five masking conditions in Kannada and Malayalam, specifically 2TB, 4TB, and SSN. The between-subject factors included two participant groups, distinguished by their exposure to the non-native (Kannada) language. Participants : Fifty-four native Malayalam speakers with normal hearing, aged between 18 and 25 years, were recruited through convenience sampling. All participants were clearly informed about the purpose and procedure of the study, and their written consent was obtained before beginning the examination and data collection. Participants’ normal hearing sensitivity was confirmed by a pure-tone average (PTA) of ≤15 dB HL, derived from thresholds at 250Hz, 500 Hz, 1 kHz, 2 kHz, 4 kHz, and 8kHz. Immittance audiometry evaluations confirmed normal middle ear function in all participants, evidenced by Type ‘A’ tympanogram and the presence of ipsilateral and contralateral acoustic reflexes in both ears. Furthermore, transient-evoked otoacoustic emissions (TEOAEs) with signal-to-noise ratios exceeding 6 dB at three consecutive frequencies validated all participants' bilateral normal outer hair cell functioning. Individuals with a history of otological or neurological conditions or exposure to occupational noise were excluded to eliminate confounding variables. The Mini-Mental State Examination (MMSE) questionnaire was administered to screen for cognitive impairments (25). Participants were categorized based on their linguistic exposure to Kannada. The non-experienced group (M = 19.4 years, SD = 1.62) included 27 first-year undergraduate students from a private university in Mangalore, Karnataka. These individuals had recently relocated to Karnataka and reported no prior academic or social exposure to the Kannada language, ensuring their status as unexposed to the regional language at the time of participation. In contrast, the Experienced group (M = 22.8 years, SD = 0.96) comprised 27 fourth-year students from the same university, all of whom had resided in Karnataka for at least three years. Their continued engagement in academic and social contexts had provided consistent exposure to Kannada. To substantiate their language experience, participants in this group completed the Language Experience and Proficiency Questionnaire (LEAP-Q) (26), which confirmed both their familiarity with and functional use of Kannada during their stay in the region. The participants were identified as proficient in the language if their score was greater than 7; the mean score for the LEAP-Q was 7.8. Approval for the study was obtained from all participants before the study began, adhering to ethical standards. Approval (INST.EC/EC/179/2024) was granted by the Institutional Ethical Committee of K. S. Hegde Medical Academy, Deralakatte, Mangalore. Stimuli: The target stimuli used in the experiment consisted of five lists of low-frequency phonetically balanced (PB) Malayalam words, which were obtained from the word list prepared by Prabhu et al. (27). High-quality and clear recordings were ensured by having three female native speakers of the Malayalam language record a sample set of PB words. The first recordings were rated by three professional raters on voice quality, clarity, and general intelligibility. According to their ratings, the best speaker for most natural and consistent speech delivery was chosen to record the entire set of target stimuli. The average pitch for the target was 182 Hz noted. The recordings were conducted in a sound-treated room that adhered to the ANSI (28) standard, which confirmed that the ambient background noise was less than 25 dBA. The microphone was positioned at 30 cm from the mouth of the speaker on an adjustable stand to provide appropriate capture of sound as well as adequate recording quality. All the recordings were carried out with Adobe Audition® software (version 2.0.5) at a sampling rate of 44.1 kHz and 24 bits resolution. For the recordings, hiss reduction and denoising operations were done on Adobe Audition® (v. 3.0) in order to improve the recording quality. Every word was exported as an individual .wav file and normalized to -3dB for root mean square (RMS) amplitude. The processed stimuli were then assessed via a Google Form by three linguistically trained native Malayalam speakers. They scored each word's clarity, naturalness, background noise, and distortion on a 4-point Likert scale (0 = Not appropriate, 1 = Somewhat appropriate, 2 = Appropriate, 3 = Highly appropriate). Words that scored less than 2 were re-recorded and re-scored until satisfactory quality was obtained. Only recordings that were rated as "appropriate" or "highly appropriate" (score ≥ 2) were used in the final stimulus set for the study. The study utilized three types of masker conditions: 2TB, 4TB, each presented in both Malayalam and Kannada, and SSN. The talker babble maskers were created using speech recordings from six native speakers in each language (three males and three females). To ensure linguistic relevance and consistency, speakers were assigned different sets of sentences drawn from standardized materials—Malayalam lists developed by Sreeraj (29) and Kannada passages compiled by Savitri and Jayaram (30). All recordings were evaluated by trained raters for naturalness, clarity, and overall speech quality. Based on these evaluations, the two male and two female speakers with the most natural and intelligible speech in each language were selected for inclusion. Recordings were done with a Behringer C-1 condenser microphone and a Behringer U-Phoria audio interface. Audio recordings were processed using Adobe Audition software to ensure consistency and clarity. Background noise was removed, the signals were normalized to -3dB using Adobe Audition, and recordings were carefully mixed to create the two-talker (2TB) and four-talker babble (4TB) maskers. The 2TB condition was created by mixing the speech stimuli of two speakers (one male and one female), while the 4TB condition combined recordings from all four selected speakers (two male and two female), producing a more complex and informationally dense masking environment. This had the effect of ensuring the masker stimuli were linguistically and acoustically balanced across both languages. For masker, the average pitch was noted for 2TB_Mal- 139 Hz, 2TB_Kan-131 Hz, 4TB_Mal-135 Hz, and 4TB_Kan-128 Hz. In order to reduce the effect of masker gender effects, male and female voices were both used in the creation of the multi-talker babble. The speakers were told to use their natural rate of speech, clarity, intonation, and stress when reading. For the speech-shaped noise (SSN) condition, representative speech samples in Malayalam and Kannada were recorded and concatenated to form the basis of the SSN stimuli. A custom MATLAB script (31) was then used to generate SSN that matched the long-term average spectrum of the original speech input. To enhance ecological validity and maximize informational masking, the multi-talker babble maskers were constructed using a mix of male and female speakers. This approach introduced greater acoustic variability in terms of pitch and voice characteristics, making the maskers more representative of natural speech environments. As a result, the listening conditions closely reflected real-world scenarios, rendering the speech perception tasks more realistic and meaningful for participants. Three audiologists with over five years of experience, rated the samples on the basis of fluency, clarity, and pronunciation using a 4-point Likert scale (0-3). Attributes like "Rate of Speech," "Duration," "Distortion," and "Naturalness" were scored from "Not Appropriate (0)" to "Totally Appropriate (3)." Ratings were collected via a Google Form, and the highest-rated sample was selected as the target stimulus. All the maskers had approximately the same energy level across the spectrum, with the overlapping long-term average spectrum of 2TB, and SSN depicted in Figure 1. The maskers utilized in the study varied in the level of linguistic information they provided. The 2TB conveyed the highest degree of linguistic content, encompassing both acoustic-phonetic and lexical-semantic information. This was followed by the 4TB, which primarily retained acoustic-phonetic cues but offered reduced lexical-semantic information due to the overlapping and less discernible speech content. In contrast, the SSN served as an energetic masker, devoid of any linguistic information, thereby isolating the effect of energetic masking on speech comprehension. Procedure: The experiment was conducted in an acoustically treated, double-room setup with ambient noise levels below 25 dBA. Using the Smriti Shravan software (Kumar and Sandeep, 2013),(32) participants were tasked with a low-frequency Malayalam PB words identification test under various masking conditions. Both the target stimuli (Malayalam words) and the different maskers (2TB, 4TB, SSN) were uploaded into the software to measure the SNR-50, the signal-to-noise ratio needed for 50% accurate word recognition. The stimuli were delivered bilaterally through a personal computer connected to an audiometer, presented at 0 dB SNR and 60 dB SPL, using Sennheiser HDA 200 headphones. The participants listened to the target word with background noise, and a closed-set response was given by the participants by selecting the perceived word on the computer screen. An adaptive procedure was used to adjust the SNR in 2-dB step sizes. A two-down and two-up rule was applied, meaning the SNR was decreased after three correct responses and increased after one incorrect response. A total of eight reversals were conducted for each condition, and the midpoint of the last four reversals was used to calculate the SNR-50 for each masking condition. Analysis: The experiment resulted in SNR-50 values obtained under all the masker conditions, where target stimuli are presented randomly in the presence of both non-native and native multi-talker babbles, as well as SSN. The SNR-50 score obtained in the SSN condition will quantify the extent of energetic masking, while the SNR-50 score in the presence of talker babble will reflect the total masking effect, which includes both informational and energetic masking. Total masking consists of both components, and the difference between scores in the multi-talker babble and SSN condition estimates masking release (MR). Subtracting the SSN score from the multi-talker babble score determines the magnitude of MR. All the data was organized and analysed using IBM SPSS Statistics (Version 29.0.2.0, IBM Corp.) software for descriptive and inferential statistics. Statistical significance was determined using a p-value less than 0.05. RESULTS This study aimed to investigate the influence of new language experience on speech perception across different masker conditions: 2TB_Mal, 2TB_Kan, 4TB_Mal, 4TB_Kan, and SSN in two groups (Experienced and non-experienced). The Shapiro-Wilk test confirmed the normal distribution of the data. The Mann-Whitney U test indicated no significant difference in PTA values between the groups (p > 0.05), indicating comparable hearing abilities. Descriptive statistics were computed for SNR-50 (in dB) to compare performance across masker type and language (Table 1). Table 1. SNR-50 means scores (in dB) and SDs of both Experienced and Non-experienced groups across the masker conditions. [M=mean; SD=standard deviation] GROUPS SNR-50 (in dB) 2TB_Mal 2TB_Kan 4TB_Mal 4TB_Kan SSN Non-Experienced M -10.34 -10.38 -8.75 -7.10 -8.76 SD 2.43 2.51 2.03 1.67 1.47 Experienced M -10.79 -9.95 -9.81 -7.36 -9.28 SD 1.69 2.21 1.47 1.47 1.40 A three-way mixed ANOVA was conducted to compare SNR-50 and MR results across two groups (non-experienced and experienced), masker noise types (2TB, 4TB, and SSN), and two masker languages (Malayalam and Kannada). Two-way interactions (1) experience*language, and (2) experience*noise type was analysed. The main effects of each factor were also evaluated. To further investigate significant main effects and interactions, pairwise comparisons were conducted with Bonferroni correction to account for multiple comparisons. SNR-50 RESULTS A three-way mixed ANOVA for the SNR-50 results revealed no significant interaction among noise type, masker language, and listener experience, [F (2, 51) = 1.082, p =0.343, η² =0.020], suggesting that the combined influence of these variables did not differentially affect speech perception. However, significant main effects were found in both masker language and noise type. Specifically, the language of the competing speech significantly influenced speech perception, [F (1, 52) = 20.587, p <0.001, η² = 0.284], indicating that participants' performance was affected depending on whether the background speech was in a familiar or unfamiliar language. Similarly, the masker noise type had a strong effect on performance, [F (2, 51) = 47.820, p <0.001, η² =0.479], with certain noise conditions creating greater difficulty in speech understanding. In contrast, listener experience did not show a significant main effect, [F (1, 52) = 1.396, p = 0.243, η² = 0.026], suggesting that prior experience with similar listening environments did not significantly impact speech perception in this study. Further two-way interactions were carried out, results stated that the interaction involving experience with noise type [F (2,51) = 1.234, p = 0.295, η² = 0.023], and language [F (1,52) = 2.413, p = 0.126, η² = 0.044] were not significant. A. Effect of Experience on SNR-50 in Different Noise Types Post-hoc comparisons revealed that for SNR-50, the experienced group demonstrated consistently better performance than the non-experienced group across all noise types (Figure 2). However, these differences were not statistically significant ( p > 0.05). These findings suggest that second-language learning may positively influence speech perception abilities, but does not significantly improve across different noise conditions. B. Effect of Experience on SNR-50 in native and non-native masker Language Pairwise comparisons indicated that the experienced group outperformed the non-experienced group across both masker languages (Figure 3). However, these differences were not statistically significant ( p > 0.05), suggesting that new language experience appeared to offer a slight advantage, particularly under the native (Malayalam) masker condition. MASKING RELEASE Descriptive statistics were computed for MR results (in dB) to compare performance across masker type and language (Table 2). Table 2. Masking release means scores (in dB) and SDs of both Experienced and Non-Experienced groups across the masker conditions. [MR= Masking release] GROUPS MASKING RELEASE (in dB) MR_2TB_Mal MR_2TB_Kan MR_4TB_Mal MR_4TB_Kan Non-Experienced M -1.50 -1.62 0.004 1.65 SD (2.51) (2.20) (2.31) (1.95) Experienced M -1.51 -0.67 -0.53 1.91 SD (1.68) (2.16) (1.58) (1.24) A three-way mixed ANOVA revealed no significant interaction among noise type, masker language, and language experience for masking release, [F (1,52) = 0.018, p = 0.892, η² = 0.000], indicating that their combined effect did not influence outcomes. Significant main effects were observed for masker language, [F (1,52) = 95.192, p < 0.001, η² = 0.647], with greater masking release observed in the non-native masker condition compared to the native masker condition, and also for noise type, [F (1,52) = 20.587, p < 0.001, η² = 0.284], where simpler noise (2TB) resulted in greater masking release than more complex noise (4TB). However, language experience did not yield a significant main effect, [F (1,52) = 0.236, p = 0.629, η² = 0.005], indicating that exposure to Kannada did not significantly influence masking release across conditions. No significant two-way interaction was found between experience and noise type, [F (1,52) = 2.273, p = 0.318, η² = 0.042], suggesting that the effect of noise complexity on masking release was not modulated by language experience. Similarly, the interaction between experience and masker language was insignificant, [F (1,52) = 2.413, p = 0.126, η² = 0.044], indicating that Kannada exposure did not significantly alter the effect of masker language on masking release. A. Effect of experience on masking release in different noise types Also, pairwise comparisons revealed that in the 2TB condition, the non-experienced group showed greater masking release, whereas in the 4TB condition, the experienced group showed greater masking release. These differences were also not statistically significant (p > 0.05) (Figure 4). Overall, the results suggest that reduced complexity of the masker enhances speech perception in the non-experienced group, while the experienced group performs better in the complex masker condition. B. Effect of experience on masking release of native and non-native maskers Also, post-hoc analysis revealed greater masking release for the experienced group in the Malayalam masker condition, whereas the non-experienced group performed better under the Kannada masker. These differences, however, did not attain statistical significance (p > 0.05) (Figure 5). Overall, the non-familiarity of a second language enhances the performance in the non-experienced group, while the experienced group has a better perception of the familiar language. DISCUSSION This study examined the effect of speech perception scores and masking release (MR) among young adults with and without exposure to a new language with low-frequency Malayalam PB words. Results showed that participants faced the greatest difficulty in the 4TB condition, followed by the SSN condition, while the 2TB condition posed the least challenge. Speech perception was generally better in Malayalam than in Kannada, indicating that non-native multi-talker babble posed a greater difficulty, whereas native-language maskers provided a relative advantage. Although the experienced group showed a better performance overall, exposure to Kannada does not significantly enhance speech perception across conditions. Effect of experience on Speech perception When comparing the effects of low-frequency PB words with different noise types, in both the experienced and non-experienced groups, the 2TB condition yielded the highest speech perception scores, followed by SSN, with the 4TB condition exerting the greatest masking effect. The superior performance in 2TB can be attributed to the presence of fewer competing voices, which enhances auditory stream segregation. With fewer interfering speakers, listeners can comprehend the linguistic content more easily. Additionally, the 2TB masker also provides greater amplitude dips and spectral gaps ( 33 ), enabling listeners to exploit brief acoustic windows providing opportunities for improved perception. Supporting this, studies by Freyman et al., 2004 ( 34 ) and Brungart et al. ( 19 ) show that speech intelligibility systematically declines as the number of competing talkers increases from two to four, primarily due to increased temporal and spectral overlap that masks the target signal more effectively. These findings also align with previous research demonstrating that fewer competing talkers produce less masking, and the spatial and temporal separation can further enhance speech understanding, especially in 2TB scenarios ( 35 , 36 ). Furthermore, linguistic maskers (4TB) imposed greater interference than non-linguistic SSN, attributed to elevated phonological and cognitive demands ( 37 ). Also, speech perception was better in SSN than in 4TB, likely due to the consistent amplitude spectrum of SSN offering less linguistic interference compared to the fluctuating nature of multi-talker babble ( 38 ). When comparing speech perception performance across all three noise conditions (2TB, 4TB, and SSN), the Experienced group demonstrated insignificant superior performance for low-frequency PB words, indicating a general advantage in challenging listening environments, highlighting a minimal advantage in processing speech under complex and adverse listening environments. Additionally, investigating performance across the two groups (experienced and non-experienced) based on masker language, the Experienced group consistently demonstrated slightly better outcomes than the non-experienced group in both non-native (Kannada) and native (Malayalam) masker conditions. This suggests that exposure to a non-native language facilitates more effective auditory stream segregation and linguistic decoding in the presence of competing speech signals. This enhancement may be attributed to the bilingual experience, which appears to strengthen both domain-specific auditory processing abilities and domain-general cognitive control mechanisms. Bilingual individuals are known to exhibit improved executive functioning, including enhanced selective attention, inhibitory control, task-switching abilities, and cognitive flexibility ( 39 ), all of which contribute to more efficient filtering of the auditory information from complex auditory maskers and enhanced focus on the target speech signal. These cognitive enhancements are particularly beneficial in demanding auditory settings, where multiple competing speakers create a high level of masking. Previous literature supports the idea that bilinguals with substantial second-language exposure possess superior speech-in-noise perception and auditory scene analysis capabilities ( 40 – 42 ). The findings of the present study align with previous research, showing that exposure to Kannada not only increases familiarity with the masker language but also helps listeners adapt more effectively to challenging listening environments that include both native and non-native speech maskers. These findings align with Cutler et al., 2004 ( 10 ) and Janse & Adank, 2012 ( 43 ), who highlighted that native-language or first-language advantages stem from stronger phonetic categorization and lexical access. Consistent with findings by Bsharat-Maalouf & Karawani, 2022, ( 44 ) bilinguals were shown to have earlier neural peaks, greater brainstem resistance, and consistent fundamental frequency (F0) representations, which are all beneficial for more effective perception of target speech in familiar and unfamiliar linguistic environments. These benefits at the neural and perceptual levels underlie the suggestion that bilingual speakers have not only perceptual but also cognitive advantages beyond language-specific processing, enabling better speech identification in challenging auditory environments with background noise. In terms of masking release across different types of background noise, the experienced group had a definite edge in masking release, particularly in the 4TB condition, reflecting their better capacity to process speech in noisy, complex environments. This superior performance would most likely be a result of the cognitive advantages that come with bilingualism, such as higher cognitive flexibility, better attentional control, and more effective working memory. Research indicates that bilinguals outperform monolinguals on linguistically complex masking tasks (e.g., 4TB) because they have better linguistic filtering ( 7 ). Additionally, research indicates that bilinguals perform better than monolinguals in linguistically complex masking situations because they have more flexible and effective linguistic filtering skills that they have developed from coping with two language systems ( 45 ). Bilinguals showed stronger subcortical encoding of speech sounds and better sustained selective attention compared to monolinguals, supporting the idea that managing two languages can sharpen both auditory and cognitive skills. Notably, Krizman et al., 2012 ( 46 ) found that attention abilities were closely linked to better speech perception in multitalker babble, highlighting the role of cognitive control in challenging listening environments. On the other hand, the non-experienced group performed better in the 2TB condition, likely because they found it easier to isolate the target speech from less linguistic and informational demands. Although the differences between groups were not statistically significant, the overall pattern points to a greater resilience in the experienced group when faced with more challenging auditory scenes. These findings suggest how bilingualism is able to improve executive functions, improve the ability to distinguish between conflicting speech, improve streams, and improve flexibility in demanding listening situations. In addition, comparing release masking between non-experienced and experienced groups for low-frequency Malayalam PB words, the trend was interesting based on the masker language. The non-experienced group showed greater masking release when the masker was in the non-native language (Kannada). Given that the non-experienced group had no prior exposure to Kannada, the unfamiliarity with the language masker likely minimizes cross-linguistic interference, allowing participants to more effectively focus on native (Malayalam) target speech and ignore the masker. In non-experienced groups, a more effective masking release in a non-native language (Kannada) corroborates with Brouwer et al., 2012 ( 47 ) results and the " Linguistic-familiarity hypothesis ," which suggests that speech perception can improve when the competing speech is a non-native language. This phenomenon may occur because listeners allocate more cognitive resources to their native language, leveraging their linguistic and semantic familiarity. This can be attributed to reduced linguistic interference, which facilitates more effective segregation of target speech ( 48 , 49 ). Consistent with previous findings, those exposed to a limited amount of a new language have a greater ability to ignore non-native interference, thus being able to understand speech more accurately in less demanding listening conditions ( 41 ). On the other hand, the experience group with approximately three years of Kannada exposure showed poorer scores in the non-native (Kannada) masker language, possibly due to the fact of incomplete acquisition of rhythm, prosody, stress, and intonation patterns of the non-native language, which are necessary to parse speech correctly. These suprasegmental features allow native speakers to distinguish the native and the non-native language for the speech perception task, but new learners will tend to have difficulty perceiving the native speech pattern, resulting in greater cognitive and linguistic interference of the masker and target, which contain meaningful linguistic information to them. The present study also highlighted the impact of using low-frequency phonetically balanced (PB) words, which are inherently more challenging to recognize. Their limited lexical familiarity, combined with their lower occurrence in everyday language, makes them more susceptible to the effects of background noise, posing a greater challenge for listeners and providing a more sensitive measure of true auditory processing abilities. Results showed better performance in the 2TB condition, followed by SSN, and lowest by 4TB, consistent with Jagadeesh & Uppunda 2022 ( 50 ), who also reported higher scores for SSN than 4TB in the Kannada sentence identification test ( 51 ). However, our findings contrast with Jagadeesh, A. B., & Uppunda, A. K., 2021 ( 52 ), who found greater masking in 2TB than 4TB for linguistic maskers with the same Kannada sentence identification test. The use of low-frequency word lists in our study revealed increased susceptibility to speech perception, especially under complex masker conditions, emphasizing the critical role of word frequency in speech perception research. The findings from the experienced group revealed slightly better speech perception of low-frequency Malayalam PB words, though the difference was not statistically significant. This may be attributed to the linguistic and phonetic similarities between the two languages studied, as both belong to the Dravidian language family. These common shared characteristics likely contributed to challenges in speech segregation, aligning with earlier research ( 16 , 53 ). The overlapping phonetic and prosodic characteristics of these languages might have made it more difficult for listeners to distinguish between them effectively. Supporting this, research by ( 54 ) revealed that bilinguals, such as English and Korean language speakers, can deal with challenging listening situations equally well as monolinguals if they use their dominant language. Similarly, Reetzke et al., 2016 ( 55 ) found that bilingualism or acquiring a new language, as English, has minimal impact on children’s auditory or language processing skills. CONCLUSION The findings of this study highlight that speech perception in noise, as measured by SNR-50 and MR, is shaped by a complex interplay of factors, including masker type, language familiarity, and the listener’s prior language experience. Overall, participants achieved the highest speech recognition scores in the two-talker babble (2TB) condition and the lowest in the four-talker babble (4TB) condition, underscoring the increased perceptual demands associated with greater masker complexity. Performance was generally better when the masker was in the participants’ native language (Malayalam) compared to the non-native (Kannada) masker, suggesting that linguistic familiarity can support more efficient speech processing in challenging listening environments. Although individuals with prior exposure to Kannada tended to perform better than those without such experience, these differences did not reach statistical significance in the present study. Nonetheless, the trend points to a possible benefit of second-language experience in enhancing auditory processing in noise. These results underscore the need for further research into how various listener-specific factors—such as language proficiency, frequency of use, vocabulary size, length of exposure, and the phonological characteristics of target words—contribute to speech-in-noise recognition. Additionally, exploring cross-linguistic influences, particularly within phonologically related languages such as those in the Dravidian family, may offer deeper insights into how linguistic overlap and similarity shape auditory processing in complex acoustic environments. Abbreviationsss PB- Phonetically balanced 2TB- 2-talker babble 4TB - 4-talker babble SSN- Speech Shaped Noise SNR- Signal-to-noise ratio MR- Masking Release PTA- Pure tone audiometry TEOAE- Transient-evoked otoacoustic emission ANSI- American National Standards Institute. RMS- Root mean square MMSE- Mini-mental state examination LEAP-Q- Language Experience and Proficiency Questionnaire F0- Fundamental Frequency Declarations Ethical approval and consent to participate Ethical approval for this study was granted by the Institutional Ethical Committee of K.S. Hegde Medical Academy (EC/NEW/INST/2022/KA/0174). Written informed consent was obtained from all participants prior to their involvement in the study. Disclosure statement The authors declare no conflicts of interest. Funding No funding was received for the present study. 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The impact of masker language and talker intelligibility on early bilinguals listeners’ speech perception. J Acoust Soc Am. 2021 Oct 1;150(4_Supplement):A44–A44. Reetzke R, Lam BPW, Xie Z, Sheng L, Chandrasekaran B. Effect of Simultaneous Bilingualism on Speech Intelligibility across Different Masker Types, Modalities, and Signal-to-Noise Ratios in School-Age Children. Elmer S, editor. PLOS ONE. 2016 Dec 9;11(12):e0168048. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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09:11:13","extension":"html","order_by":14,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":135531,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-7750783/v1/cefba8e90b8a2a5cd24b1b7b.html"},{"id":96492749,"identity":"64b61abf-4da8-4440-a464-38a3ddf32a7c","added_by":"auto","created_at":"2025-11-21 18:11:29","extension":"jpeg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":26755,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eDisplays the long-term average spectrum of the 2TB, 4TB, and SSN maskers\u003c/em\u003e\u003c/p\u003e","description":"","filename":"floatimage1.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7750783/v1/69773df82baf3e86ff9fc537.jpeg"},{"id":96492752,"identity":"9443e99d-c10c-494d-a21a-8b13445ee9e6","added_by":"auto","created_at":"2025-11-21 18:11:29","extension":"jpeg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":171479,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eSpeech perception score and SD in both language maskers across the experienced and non-experienced groups.\u003c/em\u003e\u003c/p\u003e","description":"","filename":"floatimage2.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7750783/v1/b953e0d87368eca014076776.jpeg"},{"id":96603915,"identity":"0d0f52d3-6206-472d-998a-8fcad5f8f83e","added_by":"auto","created_at":"2025-11-24 09:12:02","extension":"jpeg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":133899,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eSpeech perception score and SD in different masker noise types across both experienced and non-experienced groups.\u003c/em\u003e\u003c/p\u003e","description":"","filename":"floatimage3.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7750783/v1/2bb98f2e9dd508bc201a7f83.jpeg"},{"id":96603621,"identity":"93266585-a804-4c3c-8250-bf3b35d15978","added_by":"auto","created_at":"2025-11-24 09:10:39","extension":"jpeg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":155600,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eMasking release score and SD in both language maskers across both experienced and non-experienced groups.\u003c/em\u003e\u003c/p\u003e","description":"","filename":"floatimage4.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7750783/v1/d20c2286136a7b44d3c1bad3.jpeg"},{"id":96492760,"identity":"cddacdbb-7e50-4c4c-8803-ef7fdccb9dbd","added_by":"auto","created_at":"2025-11-21 18:11:30","extension":"jpeg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":156661,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eMasking release score and SD in different masker noise types across both experienced and non-experienced group.\u003c/em\u003e\u003c/p\u003e","description":"","filename":"floatimage5.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7750783/v1/7fd4bc809a5dcd59af10c04d.jpeg"},{"id":100990086,"identity":"3907f20c-f00f-4fdd-b363-a0b13e67d836","added_by":"auto","created_at":"2026-01-23 14:11:20","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1348627,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7750783/v1/eed47468-8fd3-46ec-b2a2-c3be90afe55c.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Speech Perception Challenges: The Role of Second Language Experience Across Masker Types in native Malayalam speakers for Low-frequency PB words","fulltext":[{"header":"INTRODUCTION","content":"\u003cp\u003eSpeech recognition involves comprehending spoken sentences by integrating auditory and cognitive processes. Communication often occurs in adverse conditions where background noise significantly impacts speech intelligibility. In such environments, speech perception is influenced by several factors, including familiarity with linguistic and phonetic patterns, background noise characteristics, noise type, speaker and listener language, and target-to-noise ratio. In noisy settings, speech perception requires heightened attention, cognitive effort, and mental focus to decode the target signal amidst distractions. As a result, understanding speech is often more difficult in these conditions than in quiet environments (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e). Competing sounds can mask important speech cues, distort phonetic details, and make it challenging to distinguish desired speech from the noisy environment.\u003c/p\u003e\u003cp\u003eNavigating complex listening situations, like multi-talker babble, often requires discerning the target speech from a mix of competing voices. Speech perception becomes especially challenging when background sounds interfere with the listener\u0026rsquo;s ability to understand speech. These effects, known as masking, can be classified into two types: energetic and informational masking. Energetic masking occurs when competing maskers physically overlap with the target signal at the auditory periphery level, making it difficult for listeners to even detect the speech. Informational masking, on the other hand, happens at a more central level, where listeners struggle to separate the target speech from similar-sounding background elements, even when the signal is audible (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e). Fortunately, listeners can sometimes overcome these challenges through a process known as masking release, which refers to the boost in perception that occurs when there are brief moments or \"dips\" in the speech babble as a masker, allowing the target speech to \"peek through\" the noise (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e). A recent study by Li et al.(\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e) suggests that masking release is even more pronounced when additional cues, like spatial separation or distinct voice characteristics, are available to help listeners focus on the target speech.\u003c/p\u003e\u003cp\u003eIn challenging environments, linguistic and phonetic overlap between the masker and target languages makes the speech perception even more challenging. Previous studies show that listeners struggle more to decode speech in noise, particularly those with limited familiarity with a masker language. Non-native listeners often have limited cues and less efficiently organized vocabulary in the second language, leading to slower and less accurate word recognition. In addition to this linguistic limitation, they must allocate more cognitive resources, such as working memory, attentional control, and processing speed, to comprehend speech. These resources are crucial for simultaneously decoding phonetic input, integrating syntactic structures, and accessing semantic meaning. In native listeners, much of this processing occurs automatically, but for non-native or bilingual individuals, it is often more effortful and resource-intensive, particularly under adverse listening conditions such as noise or competing speech. As a result, bilinguals may experience increased listening fatigue and reduced speech intelligibility in complex acoustic environments, even when they are proficient in both languages (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e). Bilinguals can differ from monolinguals in terms of the allocation of cognitive resources and the processing of language, which will impact their discrimination between speech and noise (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eAlthough a number of studies have explored the effect of linguistic and phonetic similarities, along with listener familiarity, on speech recognition in noisy scenarios, the findings have not always been consistent. Researchers suggest that greater masking release occurs when both the target word and masker languages are familiar or native to the listener (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e). Native listeners generally have an advantage in speech discrimination from background noise, likely due to stronger linguistic representations and more efficient processing (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e). Also, a study (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e) report significant differences between native and non-native listeners in consonant-vowel identification, highlighting that familiarity with language is not the sole predicting factor for speech perception. Von Hapsburg et al. (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e) further emphasized that non-native language proficiency becomes especially important when the speech signal is degraded or presented in noisy background environments. Additionally, bilingual listeners may process speech differently from monolinguals due to the demands on their cognitive resources (\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e). Interestingly, speech recognition is superior if the target and masker languages are linguistically distinct, such as English versus Mandarin, as compared to if the languages are more similar to one another, such as English and Dutch (\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e). The above experiments suggest the contributions of language familiarity and linguistic distance towards impacting perception in noise for speech.\u003c/p\u003e\u003cp\u003eAnother significant factor affecting speech perception is the background talkers. Jain et al. (\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e) identified that native Malayalam listeners were better when the masker was their native tongue, Malayalam, as opposed to Kannada, and better when there were more talkers. This better performance was presumably because there was less informational masking with more talkers. Previous research highlighted the listener\u0026rsquo;s difficulty in complex environments, such as a study (\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e) reported that increasing the number of talkers makes speech perception more difficult, mainly because of greater energetic masking. Also, Hall et al., (\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e) and Brungart et al., (\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e) found that two-talker babble disrupts speech perception more than steady noise. Signal-to-noise ratio (SNR) also impacts speech perception; Jain et al., (\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e) found that speech recognition performance deteriorates at lower SNRs (e.g., -3 dB), whereas Houben et al., (\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e) observed improved recognition as SNR increased (e.g., +\u0026thinsp;4 dB). These results highlight the intricate relationship between linguistic, cognitive, and acoustic variables in speech perception with difficult listening conditions.\u003c/p\u003e\u003cp\u003eIndia\u0026rsquo;s rich linguistic diversity creates unique listening environments that influence how individuals perceive speech. With 121 officially recognized mother tongues and six classical languages with deep historical roots (\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e), individuals are routinely exposed to both familiar and unfamiliar languages in daily communication. This rich yet complex linguistic backdrop presents challenges in measuring speech perception, especially when native and non-native maskers are involved. The present study focuses on Malayalam and Kannada due to their clinical and linguistic relevance in speech perception research. Malayalam, the participants\u0026rsquo; native language, serves as the native masker, while Kannada\u0026mdash;a widely spoken regional language in southern India\u0026mdash;functions as the non-native masker. Even though both languages belong to the Dravidian family, they differ in phonetics, prosody, rhythm, and stress patterns. These aspects make them suitable to explore the influence of familiarity on speech perception under noise. Given the multilingual situation in India, most individuals receive active or passive exposure to Kannada owing to geographical closeness, migration, and schooling, making it apt to investigate non-native babble as well as the impact of masking release on speech perception. Additionally, audiologists of varying linguistic origins in cities such as Mangalore tend to test native and non-native listeners without standardized normative reference tasks for multi-source auditory testing environments (\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eTo obtain a more accurate measure of auditory processing, low-frequency PB word lists were employed to ensure that speech perception scores in normal-hearing individuals reflected true auditory processing abilities rather than the influence of lexical familiarity or contextual prediction. These types of words minimized top-down compensatory strategies, providing a more precise and controlled assessment of the listener's ability to process lower frequency acoustic information. Using low-frequency word lists in auditory speech perception research offers several important advantages, especially for language learners. Because these words are less familiar, they can reveal subtle difficulties in speech understanding that high-frequency words often miss. (\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e) They also require learners to focus more on the actual sounds, rather than relying on guesswork or prior knowledge, encouraging deeper engagement with the language (\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e). These words reflect the real-world challenge of encountering unfamiliar vocabulary (\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e), and their acoustic features can help learners better segment and understand speech in complex listening situation. This study investigates how linguistic experience affects speech intelligibility by examining how native Malayalam speakers interpret speech under various masking conditions. The study compares individuals with and without prior exposure to Kannada to assess how familiarity with a new language influences speech recognition in noise.\u003c/p\u003e\u003cp\u003eThe present study explores the effect of learning a new language on speech perception and masking release using low-frequency Malayalam PB words in different masker conditions. Exposure to Kannada is hypothesized to increase cognitive demands and cross-linguistic interference, making speech perception more challenging in both native (Malayalam) and non-native (Kannada) background noise. In contrast, individuals without Kannada exposure are expected to show better speech perception in the Kannada background due to reduced cross-language interference. The objectives of the study include measuring how a new language experience (exposure to Kannada) influences speech perception abilities (SNR-50 and MR) by comparing experienced and non-experienced Malayalam speakers across different types of noise maskers specifically two-talker babble (2TB), four-talker babble (4TB), speech-shaped noise (SSN) and between native (Malayalam) and non-native (Kannada) language masker conditions.\u003c/p\u003e"},{"header":"METHODOLOGY","content":"\u003cp\u003eThe study utilized a 5x2 cross-sectional mixed design, integrating within-subject and between-subject factors. The within-subject factors encompassed five masking conditions in Kannada and Malayalam, specifically 2TB, 4TB, and SSN. The between-subject factors included two participant groups, distinguished by their exposure to the non-native (Kannada) language.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eParticipants\u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003e:\u0026nbsp;\u003c/strong\u003eFifty-four native Malayalam speakers with normal hearing, aged between 18 and 25 years, were recruited through convenience sampling. All participants were clearly informed about the purpose and procedure of the study, and their written consent was obtained before beginning the examination and data collection. \u0026nbsp;Participants\u0026rsquo; normal hearing sensitivity was confirmed by a pure-tone average (PTA) of \u0026le;15 dB HL, derived from thresholds at 250Hz, 500 Hz, 1 kHz, 2 kHz, 4 kHz, and 8kHz. Immittance audiometry evaluations confirmed normal middle ear function in all participants, evidenced by Type \u0026lsquo;A\u0026rsquo; tympanogram and the presence of ipsilateral and contralateral acoustic reflexes in both ears. Furthermore, transient-evoked otoacoustic emissions (TEOAEs) with signal-to-noise ratios exceeding 6 dB at three consecutive frequencies validated all participants\u0026apos; bilateral normal outer hair cell functioning. Individuals with a history of otological or neurological conditions or exposure to occupational noise were excluded to eliminate confounding variables. The Mini-Mental State Examination (MMSE) questionnaire was administered to screen for cognitive impairments (25).\u003c/p\u003e\n\u003cp\u003eParticipants were categorized based on their linguistic exposure to Kannada. The non-experienced group (M = 19.4 years, SD = 1.62) included 27 first-year undergraduate students from a private university in Mangalore, Karnataka. These individuals had recently relocated to Karnataka and reported no prior academic or social exposure to the Kannada language, ensuring their status as unexposed to the regional language at the time of participation. In contrast, the Experienced group (M = 22.8 years, SD = 0.96) comprised 27 fourth-year students from the same university, all of whom had resided in Karnataka for at least three years. Their continued engagement in academic and social contexts had provided consistent exposure to Kannada. To substantiate their language experience, participants in this group completed the Language Experience and Proficiency Questionnaire (LEAP-Q) (26), which confirmed both their familiarity with and functional use of Kannada during their stay in the region. The participants were identified as proficient in the language if their score was greater than 7; the mean score for the LEAP-Q was 7.8. Approval for the study was obtained from all participants before the study began, adhering to ethical standards. Approval (INST.EC/EC/179/2024) was granted by the Institutional Ethical Committee of K. S. Hegde Medical Academy, Deralakatte, Mangalore.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eStimuli:\u003c/em\u003e\u003c/strong\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003eThe target stimuli used in the experiment consisted of five lists of low-frequency phonetically balanced (PB) Malayalam words, which were obtained from the word list prepared by Prabhu et al. (27). High-quality and clear recordings were ensured by having three female native speakers of the Malayalam language record a sample set of PB words. The first recordings were rated by three professional raters on voice quality, clarity, and general intelligibility. According to their ratings, the best speaker for most natural and consistent speech delivery was chosen to record the entire set of target stimuli. The average pitch for the target was 182 Hz noted.\u003c/p\u003e\n\u003cp\u003eThe recordings were conducted in a sound-treated room that adhered to the ANSI (28) standard, which confirmed that the ambient background noise was less than 25 dBA. \u0026nbsp;The microphone was positioned at 30 cm from the mouth of the speaker on an adjustable stand to provide appropriate capture of sound as well as adequate recording quality. All the recordings were carried out with Adobe Audition\u0026reg; software (version 2.0.5) at a sampling rate of 44.1 kHz and 24 bits resolution. For the recordings, hiss reduction and denoising operations were done on Adobe Audition\u0026reg; (v. 3.0) in order to improve the recording quality. Every word was exported as an individual .wav file and normalized to -3dB for root mean square (RMS) amplitude. The processed stimuli were then assessed via a Google Form by three linguistically trained native Malayalam speakers. They scored each word\u0026apos;s clarity, naturalness, background noise, and distortion on a 4-point Likert scale (0 = Not appropriate, 1 = Somewhat appropriate, 2 = Appropriate, 3 = Highly appropriate). Words that scored less than 2 were re-recorded and re-scored until satisfactory quality was obtained. Only recordings that were rated as \u0026quot;appropriate\u0026quot; or \u0026quot;highly appropriate\u0026quot; (score \u0026ge; 2) were used in the final stimulus set for the study.\u003c/p\u003e\n\u003cp\u003eThe study utilized three types of masker conditions: 2TB, 4TB, each presented in both Malayalam and Kannada, and SSN. The talker babble maskers were created using speech recordings from six native speakers in each language (three males and three females). To ensure linguistic relevance and consistency, speakers were assigned different sets of sentences drawn from standardized materials\u0026mdash;Malayalam lists developed by Sreeraj (29) and Kannada passages compiled by Savitri and Jayaram (30). All recordings were evaluated by trained raters for naturalness, clarity, and overall speech quality. Based on these evaluations, the two male and two female speakers with the most natural and intelligible speech in each language were selected for inclusion. Recordings were done with a Behringer C-1 condenser microphone and a Behringer U-Phoria audio interface. Audio recordings were processed using Adobe Audition software to ensure consistency and clarity. Background noise was removed, the signals were normalized to -3dB using Adobe Audition, and recordings were carefully mixed to create the two-talker (2TB) and four-talker babble (4TB) maskers. The 2TB condition was created by mixing the speech stimuli of two speakers (one male and one female), while the 4TB condition combined recordings from all four selected speakers (two male and two female), producing a more complex and informationally dense masking environment. This had the effect of ensuring the masker stimuli were linguistically and acoustically balanced across both languages. For masker, the average pitch was noted for 2TB_Mal- 139 Hz, 2TB_Kan-131 Hz, 4TB_Mal-135 Hz, and 4TB_Kan-128 Hz. In order to reduce the effect of masker gender effects, male and female voices were both used in the creation of the multi-talker babble. The speakers were told to use their natural rate of speech, clarity, intonation, and stress when reading. For the speech-shaped noise (SSN) condition, representative speech samples in Malayalam and Kannada were recorded and concatenated to form the basis of the SSN stimuli. A custom MATLAB script \u0026nbsp;(31) was then used to generate SSN that matched the long-term average spectrum of the original speech input. To enhance ecological validity and maximize informational masking, the multi-talker babble maskers were constructed using a mix of male and female speakers. This approach introduced greater acoustic variability in terms of pitch and voice characteristics, making the maskers more representative of natural speech environments. As a result, the listening conditions closely reflected real-world scenarios, rendering the speech perception tasks more realistic and meaningful for participants.\u003c/p\u003e\n\u003cp\u003eThree audiologists with over five years of experience, rated the samples on the basis of fluency, clarity, and pronunciation using a 4-point Likert scale (0-3). Attributes like \u0026quot;Rate of Speech,\u0026quot; \u0026quot;Duration,\u0026quot; \u0026quot;Distortion,\u0026quot; and \u0026quot;Naturalness\u0026quot; were scored from \u0026quot;Not Appropriate (0)\u0026quot; to \u0026quot;Totally Appropriate (3).\u0026quot; Ratings were collected via a Google Form, and the highest-rated sample was selected as the target stimulus. All the maskers had approximately the same energy level across the spectrum, with the overlapping long-term average spectrum of 2TB, and SSN depicted in Figure 1.\u003c/p\u003e\n\u003cp\u003eThe maskers utilized in the study varied in the level of linguistic information they provided. The 2TB conveyed the highest degree of linguistic content, encompassing both acoustic-phonetic and lexical-semantic information. This was followed by the 4TB, which primarily retained acoustic-phonetic cues but offered reduced lexical-semantic information due to the overlapping and less discernible speech content. In contrast, the SSN served as an energetic masker, devoid of any linguistic information, thereby isolating the effect of energetic masking on speech comprehension.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eProcedure:\u003c/em\u003e\u003c/strong\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003eThe experiment was conducted in an acoustically treated, double-room setup with ambient noise levels below 25 dBA. Using the Smriti Shravan software (Kumar and Sandeep, 2013),(32) participants were tasked with a low-frequency Malayalam PB words identification test under various masking conditions. Both the target stimuli (Malayalam words) and the different maskers (2TB, 4TB, SSN) were uploaded into the software to measure the SNR-50, the signal-to-noise ratio needed for 50% accurate word recognition. The stimuli were delivered bilaterally through a personal computer connected to an audiometer, presented at 0 dB SNR and 60 dB SPL, using Sennheiser HDA 200 headphones. The participants listened to the target word with background noise, and a closed-set response was given by the participants by selecting the perceived word on the computer screen. An adaptive procedure was used to adjust the SNR in 2-dB step sizes. A two-down and two-up rule was applied, meaning the SNR was decreased after three correct responses and increased after one incorrect response. A total of eight reversals were conducted for each condition, and the midpoint of the last four reversals was used to calculate the SNR-50 for each masking condition.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eAnalysis:\u0026nbsp;\u003c/em\u003e\u003c/strong\u003eThe experiment resulted in SNR-50 values obtained under all the masker conditions, where target stimuli are presented randomly in the presence of both non-native and native multi-talker babbles, as well as SSN. The SNR-50 score obtained in the SSN condition will quantify the extent of energetic masking, while the SNR-50 score in the presence of talker babble will reflect the total masking effect, which includes both informational and energetic masking. Total masking consists of both components, and the difference between scores in the multi-talker babble and SSN condition estimates masking release (MR). Subtracting the SSN score from the multi-talker babble score determines the magnitude of MR. All the data was organized and analysed using IBM SPSS Statistics (Version 29.0.2.0, IBM Corp.) software for descriptive and inferential statistics. Statistical significance was determined using a p-value less than 0.05.\u003c/p\u003e"},{"header":"RESULTS","content":"\u003cp\u003eThis study aimed to investigate the influence of new language experience on speech perception across different masker conditions: 2TB_Mal, 2TB_Kan, 4TB_Mal, 4TB_Kan, and SSN in two groups (Experienced and non-experienced). The Shapiro-Wilk test confirmed the normal distribution of the data. The Mann-Whitney U test indicated no significant difference in PTA values between the groups (p \u0026gt; 0.05), indicating comparable hearing abilities. Descriptive statistics were computed for SNR-50 (in dB) to compare performance across masker type and language (Table 1).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eTable 1. SNR-50 means scores (in dB) and SDs of both Experienced and Non-experienced groups across the masker conditions. [M=mean; SD=standard deviation]\u003c/em\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"601\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" rowspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003eGROUPS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"5\" valign=\"top\"\u003e\n \u003cp\u003eSNR-50 (in dB)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e2TB_Mal\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e2TB_Kan\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e4TB_Mal\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e4TB_Kan\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eSSN\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003eNon-Experienced\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eM\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-10.34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-10.38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-8.75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-7.10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-8.76\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eSD\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2.43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2.51\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.67\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.47\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003eExperienced\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eM\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-10.79\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-9.95\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-9.81\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-7.36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-9.28\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eSD\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.69\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2.21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.47\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.47\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.40\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eA three-way mixed ANOVA was conducted to compare SNR-50 and MR results across two groups (non-experienced and experienced), masker noise types (2TB, 4TB, and SSN), and two masker languages (Malayalam and Kannada). Two-way interactions (1) experience*language, and (2) experience*noise type was analysed. The main effects of each factor were also evaluated. To further investigate significant main effects and interactions, pairwise comparisons were conducted with Bonferroni correction to account for multiple comparisons.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSNR-50 RESULTS\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA three-way mixed ANOVA for the SNR-50 results revealed no significant interaction among noise type, masker language, and listener experience, [F (2, 51) = 1.082, p =0.343, \u0026eta;\u0026sup2; =0.020], suggesting that the combined influence of these variables did not differentially affect speech perception. However, significant main effects were found in both masker language and noise type. Specifically, the language of the competing speech significantly influenced speech perception, [F (1, 52) = 20.587, p \u0026lt;0.001, \u0026eta;\u0026sup2; = 0.284], indicating that participants\u0026apos; performance was affected depending on whether the background speech was in a familiar or unfamiliar language. Similarly, the masker noise type had a strong effect on performance, [F (2, 51) = 47.820, p \u0026lt;0.001, \u0026eta;\u0026sup2; =0.479], with certain noise conditions creating greater difficulty in speech understanding. In contrast, listener experience did not show a significant main effect, [F (1, 52) = 1.396, p = 0.243, \u0026eta;\u0026sup2; = 0.026], suggesting that prior experience with similar listening environments did not significantly impact speech perception in this study. Further two-way interactions were carried out, results stated that the interaction involving experience with noise type [F (2,51) = 1.234, p = 0.295, \u0026eta;\u0026sup2; = 0.023], and language [F (1,52) = 2.413, p = 0.126, \u0026eta;\u0026sup2; = 0.044] were not significant.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eA. Effect of Experience on SNR-50 in Different Noise Types\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ePost-hoc comparisons revealed that for SNR-50, the experienced group demonstrated consistently better performance than the non-experienced group across all noise types (Figure 2). However, these differences were not statistically significant (\u003cem\u003ep\u003c/em\u003e \u0026gt; 0.05). These findings suggest that second-language learning may positively influence speech perception abilities, but does not significantly improve across different noise conditions.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eB. Effect of Experience on SNR-50 in native and non-native masker Language\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ePairwise comparisons indicated that the experienced group outperformed the non-experienced group across both masker languages (Figure 3). However, these differences were not statistically significant (\u003cem\u003ep\u003c/em\u003e \u0026gt; 0.05), suggesting that new language experience appeared to offer a slight advantage, particularly under the native (Malayalam) masker condition.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMASKING RELEASE\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eDescriptive statistics were computed for MR results (in dB) to compare performance across masker type and language (Table 2).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eTable 2. Masking release means scores (in dB) and SDs of both Experienced and Non-Experienced groups across the masker conditions. \u0026nbsp;[MR= Masking release]\u003c/em\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"100%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" rowspan=\"2\" valign=\"top\" style=\"width: 23px;\"\u003e\n \u003cp\u003eGROUPS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"4\" valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003eMASKING RELEASE (in dB)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMR_2TB_Mal\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMR_2TB_Kan\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMR_4TB_Mal\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMR_4TB_Kan\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 17px;\"\u003e\n \u003cp\u003eNon-Experienced\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eM\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e-1.50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e-1.62\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e0.004\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e1.65\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 5px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSD\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e(2.51)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e(2.20)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e(2.31)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e(1.95)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 17px;\"\u003e\n \u003cp\u003eExperienced\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eM\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e-1.51\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e-0.67\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e-0.53\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e1.91\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 5px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSD\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e(1.68)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e(2.16)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e(1.58)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e(1.24)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eA three-way mixed ANOVA revealed no significant interaction among noise type, masker language, and language experience for masking release, [F (1,52) = 0.018, p = 0.892, \u0026eta;\u0026sup2; = 0.000], indicating that their combined effect did not influence outcomes. Significant main effects were observed for masker language, [F (1,52) = 95.192, p \u0026lt; 0.001, \u0026eta;\u0026sup2; = 0.647], with greater masking release observed in the non-native masker condition compared to the native masker condition, and also for noise type, [F (1,52) = 20.587, p \u0026lt; 0.001, \u0026eta;\u0026sup2; = 0.284], where simpler noise (2TB) resulted in greater masking release than more complex noise (4TB). However, language experience did not yield a significant main effect, [F (1,52) = 0.236, p = 0.629, \u0026eta;\u0026sup2; = 0.005], indicating that exposure to Kannada did not significantly influence masking release across conditions. No significant two-way interaction was found between experience and noise type, [F (1,52) = 2.273, p = 0.318, \u0026eta;\u0026sup2; = 0.042], suggesting that the effect of noise complexity on masking release was not modulated by language experience. Similarly, the interaction between experience and masker language was insignificant, [F (1,52) = 2.413, p = 0.126, \u0026eta;\u0026sup2; = 0.044], indicating that Kannada exposure did not significantly alter the effect of masker language on masking release.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eA. Effect of experience on masking release in different noise types\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAlso, pairwise comparisons revealed that in the 2TB condition, the non-experienced group showed greater masking release, whereas in the 4TB condition, the experienced group showed greater masking release. These differences were also not statistically significant (p \u0026gt; 0.05) (Figure 4). Overall, the results suggest that reduced complexity of the masker enhances speech perception in the non-experienced group, while the experienced group performs better in the complex masker condition.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eB. Effect of experience on masking release of native and non-native maskers\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAlso, post-hoc analysis revealed greater masking release for the experienced group in the Malayalam masker condition, whereas the non-experienced group performed better under the Kannada masker. These differences, however, did not attain statistical significance (p \u0026gt; 0.05) (Figure 5). Overall, the non-familiarity of a second language enhances the performance in the non-experienced group, while the experienced group has a better perception of the familiar language.\u003c/p\u003e"},{"header":"DISCUSSION","content":"\u003cp\u003eThis study examined the effect of speech perception scores and masking release (MR) among young adults with and without exposure to a new language with low-frequency Malayalam PB words. Results showed that participants faced the greatest difficulty in the 4TB condition, followed by the SSN condition, while the 2TB condition posed the least challenge. Speech perception was generally better in Malayalam than in Kannada, indicating that non-native multi-talker babble posed a greater difficulty, whereas native-language maskers provided a relative advantage. Although the experienced group showed a better performance overall, exposure to Kannada does not significantly enhance speech perception across conditions.\u003c/p\u003e\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\u003ch2\u003eEffect of experience on Speech perception\u003c/h2\u003e\u003cp\u003e When comparing the effects of low-frequency PB words with different noise types, in both the experienced and non-experienced groups, the 2TB condition yielded the highest speech perception scores, followed by SSN, with the 4TB condition exerting the greatest masking effect. The superior performance in 2TB can be attributed to the presence of fewer competing voices, which enhances auditory stream segregation. With fewer interfering speakers, listeners can comprehend the linguistic content more easily. Additionally, the 2TB masker also provides greater amplitude dips and spectral gaps (\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e), enabling listeners to exploit brief acoustic windows providing opportunities for improved perception. Supporting this, studies by Freyman et al., 2004 (\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e) and Brungart et al. (\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e) show that speech intelligibility systematically declines as the number of competing talkers increases from two to four, primarily due to increased temporal and spectral overlap that masks the target signal more effectively. These findings also align with previous research demonstrating that fewer competing talkers produce less masking, and the spatial and temporal separation can further enhance speech understanding, especially in 2TB scenarios (\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e). Furthermore, linguistic maskers (4TB) imposed greater interference than non-linguistic SSN, attributed to elevated phonological and cognitive demands (\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e). Also, speech perception was better in SSN than in 4TB, likely due to the consistent amplitude spectrum of SSN offering less linguistic interference compared to the fluctuating nature of multi-talker babble (\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eWhen comparing speech perception performance across all three noise conditions (2TB, 4TB, and SSN), the Experienced group demonstrated insignificant superior performance for low-frequency PB words, indicating a general advantage in challenging listening environments, highlighting a minimal advantage in processing speech under complex and adverse listening environments. Additionally, investigating performance across the two groups (experienced and non-experienced) based on masker language, the Experienced group consistently demonstrated slightly better outcomes than the non-experienced group in both non-native (Kannada) and native (Malayalam) masker conditions. This suggests that exposure to a non-native language facilitates more effective auditory stream segregation and linguistic decoding in the presence of competing speech signals. This enhancement may be attributed to the bilingual experience, which appears to strengthen both domain-specific auditory processing abilities and domain-general cognitive control mechanisms.\u003c/p\u003e\u003cp\u003eBilingual individuals are known to exhibit improved executive functioning, including enhanced selective attention, inhibitory control, task-switching abilities, and cognitive flexibility (\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e), all of which contribute to more efficient filtering of the auditory information from complex auditory maskers and enhanced focus on the target speech signal. These cognitive enhancements are particularly beneficial in demanding auditory settings, where multiple competing speakers create a high level of masking. Previous literature supports the idea that bilinguals with substantial second-language exposure possess superior speech-in-noise perception and auditory scene analysis capabilities (\u003cspan additionalcitationids=\"CR41\" citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eThe findings of the present study align with previous research, showing that exposure to Kannada not only increases familiarity with the masker language but also helps listeners adapt more effectively to challenging listening environments that include both native and non-native speech maskers. These findings align with Cutler et al., 2004 (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e) and Janse \u0026amp; Adank, 2012 (\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e), who highlighted that native-language or first-language advantages stem from stronger phonetic categorization and lexical access. Consistent with findings by Bsharat-Maalouf \u0026amp; Karawani, 2022, (\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e) bilinguals were shown to have earlier neural peaks, greater brainstem resistance, and consistent fundamental frequency (F0) representations, which are all beneficial for more effective perception of target speech in familiar and unfamiliar linguistic environments. These benefits at the neural and perceptual levels underlie the suggestion that bilingual speakers have not only perceptual but also cognitive advantages beyond language-specific processing, enabling better speech identification in challenging auditory environments with background noise.\u003c/p\u003e\u003cp\u003eIn terms of masking release across different types of background noise, the experienced group had a definite edge in masking release, particularly in the 4TB condition, reflecting their better capacity to process speech in noisy, complex environments. This superior performance would most likely be a result of the cognitive advantages that come with bilingualism, such as higher cognitive flexibility, better attentional control, and more effective working memory. Research indicates that bilinguals outperform monolinguals on linguistically complex masking tasks (e.g., 4TB) because they have better linguistic filtering (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e). Additionally, research indicates that bilinguals perform better than monolinguals in linguistically complex masking situations because they have more flexible and effective linguistic filtering skills that they have developed from coping with two language systems (\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e). Bilinguals showed stronger subcortical encoding of speech sounds and better sustained selective attention compared to monolinguals, supporting the idea that managing two languages can sharpen both auditory and cognitive skills. Notably, Krizman et al., 2012 (\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e) found that attention abilities were closely linked to better speech perception in multitalker babble, highlighting the role of cognitive control in challenging listening environments. On the other hand, the non-experienced group performed better in the 2TB condition, likely because they found it easier to isolate the target speech from less linguistic and informational demands. Although the differences between groups were not statistically significant, the overall pattern points to a greater resilience in the experienced group when faced with more challenging auditory scenes. These findings suggest how bilingualism is able to improve executive functions, improve the ability to distinguish between conflicting speech, improve streams, and improve flexibility in demanding listening situations.\u003c/p\u003e\u003cp\u003eIn addition, comparing release masking between non-experienced and experienced groups for low-frequency Malayalam PB words, the trend was interesting based on the masker language. The non-experienced group showed greater masking release when the masker was in the non-native language (Kannada). Given that the non-experienced group had no prior exposure to Kannada, the unfamiliarity with the language masker likely minimizes cross-linguistic interference, allowing participants to more effectively focus on native (Malayalam) target speech and ignore the masker. In non-experienced groups, a more effective masking release in a non-native language (Kannada) corroborates with Brouwer et al., 2012 (\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e) results and the \"\u003cem\u003eLinguistic-familiarity hypothesis\u003c/em\u003e,\" which suggests that speech perception can improve when the competing speech is a non-native language. This phenomenon may occur because listeners allocate more cognitive resources to their native language, leveraging their linguistic and semantic familiarity. This can be attributed to reduced linguistic interference, which facilitates more effective segregation of target speech (\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e, \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e). Consistent with previous findings, those exposed to a limited amount of a new language have a greater ability to ignore non-native interference, thus being able to understand speech more accurately in less demanding listening conditions (\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e). On the other hand, the experience group with approximately three years of Kannada exposure showed poorer scores in the non-native (Kannada) masker language, possibly due to the fact of incomplete acquisition of rhythm, prosody, stress, and intonation patterns of the non-native language, which are necessary to parse speech correctly. These suprasegmental features allow native speakers to distinguish the native and the non-native language for the speech perception task, but new learners will tend to have difficulty perceiving the native speech pattern, resulting in greater cognitive and linguistic interference of the masker and target, which contain meaningful linguistic information to them.\u003c/p\u003e\u003cp\u003eThe present study also highlighted the impact of using low-frequency phonetically balanced (PB) words, which are inherently more challenging to recognize. Their limited lexical familiarity, combined with their lower occurrence in everyday language, makes them more susceptible to the effects of background noise, posing a greater challenge for listeners and providing a more sensitive measure of true auditory processing abilities. Results showed better performance in the 2TB condition, followed by SSN, and lowest by 4TB, consistent with Jagadeesh \u0026amp; Uppunda 2022 (\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e), who also reported higher scores for SSN than 4TB in the Kannada sentence identification test (\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e). However, our findings contrast with Jagadeesh, A. B., \u0026amp; Uppunda, A. K., 2021 (\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e), who found greater masking in 2TB than 4TB for linguistic maskers with the same Kannada sentence identification test. The use of low-frequency word lists in our study revealed increased susceptibility to speech perception, especially under complex masker conditions, emphasizing the critical role of word frequency in speech perception research.\u003c/p\u003e\u003cp\u003eThe findings from the experienced group revealed slightly better speech perception of low-frequency Malayalam PB words, though the difference was not statistically significant. This may be attributed to the linguistic and phonetic similarities between the two languages studied, as both belong to the Dravidian language family. These common shared characteristics likely contributed to challenges in speech segregation, aligning with earlier research (\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e). The overlapping phonetic and prosodic characteristics of these languages might have made it more difficult for listeners to distinguish between them effectively. Supporting this, research by (\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e) revealed that bilinguals, such as English and Korean language speakers, can deal with challenging listening situations equally well as monolinguals if they use their dominant language. Similarly, Reetzke et al., 2016 (\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e) found that bilingualism or acquiring a new language, as English, has minimal impact on children\u0026rsquo;s auditory or language processing skills.\u003c/p\u003e\u003c/div\u003e"},{"header":"CONCLUSION","content":"\u003cp\u003eThe findings of this study highlight that speech perception in noise, as measured by SNR-50 and MR, is shaped by a complex interplay of factors, including masker type, language familiarity, and the listener’s prior language experience. Overall, participants achieved the highest speech recognition scores in the two-talker babble (2TB) condition and the lowest in the four-talker babble (4TB) condition, underscoring the increased perceptual demands associated with greater masker complexity. Performance was generally better when the masker was in the participants’ native language (Malayalam) compared to the non-native (Kannada) masker, suggesting that linguistic familiarity can support more efficient speech processing in challenging listening environments. Although individuals with prior exposure to Kannada tended to perform better than those without such experience, these differences did not reach statistical significance in the present study. Nonetheless, the trend points to a possible benefit of second-language experience in enhancing auditory processing in noise.\u003c/p\u003e\u003cp\u003eThese results underscore the need for further research into how various listener-specific factors—such as language proficiency, frequency of use, vocabulary size, length of exposure, and the phonological characteristics of target words—contribute to speech-in-noise recognition. Additionally, exploring cross-linguistic influences, particularly within phonologically related languages such as those in the Dravidian family, may offer deeper insights into how linguistic overlap and similarity shape auditory processing in complex acoustic environments.\u003c/p\u003e"},{"header":"Abbreviationsss","content":"\u003cp\u003e\u003cstrong\u003ePB-\u003c/strong\u003e Phonetically balanced\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2TB-\u003c/strong\u003e 2-talker babble\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e4TB\u003c/strong\u003e- 4-talker babble\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSSN-\u003c/strong\u003e Speech Shaped Noise\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSNR-\u003c/strong\u003e Signal-to-noise ratio\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMR-\u003c/strong\u003e Masking Release\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePTA-\u003c/strong\u003e Pure tone audiometry\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTEOAE-\u003c/strong\u003e Transient-evoked otoacoustic emission\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eANSI-\u003c/strong\u003e American National Standards Institute.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eRMS-\u003c/strong\u003e Root mean square\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMMSE-\u003c/strong\u003e Mini-mental state examination\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eLEAP-Q-\u003c/strong\u003e Language Experience and Proficiency Questionnaire\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eF0-\u003c/strong\u003e Fundamental Frequency\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthical approval and consent to participate\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eEthical approval for this study was granted by the Institutional Ethical Committee of K.S. Hegde Medical Academy (EC/NEW/INST/2022/KA/0174). Written informed consent was obtained from all participants prior to their involvement in the study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDisclosure statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no conflicts of interest.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNo funding was received for the present study.\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003ePradeep Kumar Mahapatra: Data Collection, Interpretation of data, Drafting of Manuscript, and Statistical analysis; Dhananjay Rachana: Study design, Supervision, Data interpretation, and Critical revision of manuscript; Ritwik Prakash, Ashish Bisht: Data collection, co-writing of the manuscript.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eThe datasets generated during and/or analysed during the current study are available from the corresponding author on reasonable request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eCooke M. 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Front Psychol [Internet]. 2014 Jul 2 [cited 2025 Apr 14];5. Available from: http://journal.frontiersin.org/article/10.3389/fpsyg.2014.00651/abstract\u003c/li\u003e\n\u003cli\u003eFlorentine M, Buus S, Scharf B, Canevet G. Speech reception thresholds in noise for native and non-native listeners. J Acoust Soc Am. 1984 May 1;75(S1):S84\u0026ndash;S84. \u003c/li\u003e\n\u003cli\u003eVan Engen KJ. Similarity and familiarity: Second language sentence recognition in first- and second-language multi-talker babble. Speech Commun. 2010 Nov;52(11\u0026ndash;12):943\u0026ndash;53. \u003c/li\u003e\n\u003cli\u003eRogers CL, Lister JJ, Febo DM, Besing JM, Abrams HB. Effects of bilingualism, noise, and reverberation on speech perception by listeners with normal hearing. Appl Psycholinguist. 2006 Jul;27(3):465\u0026ndash;85. \u003c/li\u003e\n\u003cli\u003eMayo LH, Florentine M, Buus S. Age of Second-Language Acquisition and Perception of Speech in Noise. 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DEVELOPMENT AND STANDARDIZATION OFAUDITORY LOW-FREQUENCY WORD LISTS IN HINDI. J Hear Sci. 2016 Dec 31;6(4):39\u0026ndash;49. \u003c/li\u003e\n\u003cli\u003eCitrayasa V, Marsella E, Nernere MS. Strategies of vocabulary learning employed by low-frequency-word level students in international class. J Engl Foreign Lang. 2022 Mar 14;12(1):97\u0026ndash;116. \u003c/li\u003e\n\u003cli\u003eMathuranath, P. S., Cherian, J. P., Mathew, R., George, A., Alexander, A., \u0026amp; Sarma, S. P. Mini mental state examination and the Addenbrooke\u0026rsquo;s cognitive examination: Effect of education and norms for a multicultural population. Neurology India, 55(2), 106-110. 2007; \u003c/li\u003e\n\u003cli\u003eMaitreyee, R., \u0026amp; Goswami, S. P. Language proficiency questionnaire: an adaptation of LEAP-Q in Indian context. Unpublished dissertation. 2009; \u003c/li\u003e\n\u003cli\u003ePrabhu, P. Unpubl ARF Proj. Unpubl ARF Proj. University of Mysore. 2017. \u003c/li\u003e\n\u003cli\u003eANSI S. 13. Noise AM. Levels for Audiometric Test Rooms. Washington, DC: American National Standards Institute. 2008. \u003c/li\u003e\n\u003cli\u003eKonadath S. Sentence lists in malayalam and telugu. Unpublished ARF Project. All India Institute of Speech and Hearing, Mysuru. 2013; \u003c/li\u003e\n\u003cli\u003eSavithri Jayaram, S. R. 300-word reading passages in Dravidian languages. ARF Project. All India Institute of Speech and Hearing. Mysuru. 2005. \u003c/li\u003e\n\u003cli\u003eGnanateja, G. N. Speech in noise mixing, signal to noise ratio. Matlab Central File Exchange. http://www.mathworks.com/matlabcentral/fileexchange/37842-speech-in-noise-mixingsignal-to-noise-ratio. 2012. \u003c/li\u003e\n\u003cli\u003eKumar AU, Sandeep M. Auditory cognitive training module. ARF funded departmental project submitted to All India Institute of Speech and Hearing, Mysore. 2013; \u003c/li\u003e\n\u003cli\u003eDarwin CJ. 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Bilingual and monolingual processing of competing lexical items. Appl Psycholinguist. 2003 Jun;24(2):173\u0026ndash;93. \u003c/li\u003e\n\u003cli\u003eCosta A, Hern\u0026aacute;ndez M, Costa-Faidella J, Sebasti\u0026aacute;n-Gall\u0026eacute;s N. On the bilingual advantage in conflict processing: Now you see it, now you don\u0026rsquo;t. Cognition. 2009 Nov;113(2):135\u0026ndash;49. \u003c/li\u003e\n\u003cli\u003eJanse E, Adank P. Predicting foreign-accent adaptation in older adults. Q J Exp Psychol. 2012 Aug;65(8):1563\u0026ndash;85. \u003c/li\u003e\n\u003cli\u003eBsharat-Maalouf D, Karawani H. Bilinguals\u0026rsquo; speech perception in noise: Perceptual and neural associations. Prabhu P, editor. PLOS ONE. 2022 Feb 23;17(2):e0264282. \u003c/li\u003e\n\u003cli\u003eLew E, Hallot S, Byers-Heinlein K, Deroche M. Navigating the bilingual cocktail party: a critical role for listeners\u0026rsquo; L1 in the linguistic aspect of informational masking. Biling Lang Cogn. 2025 May;28(3):748\u0026ndash;56. ss\u003c/li\u003e\n\u003cli\u003eKrizman J, Marian V, Shook A, Skoe E, Kraus N. Subcortical encoding of sound is enhanced in bilinguals and relates to executive function advantages. Proc Natl Acad Sci. 2012 May 15;109(20):7877\u0026ndash;81. \u003c/li\u003e\n\u003cli\u003eBrouwer S, Mitterer H, Huettig F. Can hearing \u003cem\u003eputer\u003c/em\u003e activate \u003cem\u003epupil\u003c/em\u003e ? Phonological competition and the processing of reduced spoken words in spontaneous conversations. Q J Exp Psychol. 2012 Nov;65(11):2193\u0026ndash;220. \u003c/li\u003e\n\u003cli\u003eRhebergen KS, Versfeld NJ, Dreschler WouterA. Release from informational masking by time reversal of native and non-native interfering speech. J Acoust Soc Am. 2005 Sep 1;118(3):1274\u0026ndash;7. \u003c/li\u003e\n\u003cli\u003eMattys SL, Davis MH, Bradlow AR, Scott SK. Speech recognition in adverse conditions: A review. Lang Cogn Process. 2012 Sep;27(7\u0026ndash;8):953\u0026ndash;78. \u003c/li\u003e\n\u003cli\u003eJagadeesh AB, Uppunda AK. Effect of Age on Informational Masking: Differential Effects of Phonetic and Semantic Information in the Masker. Am J Audiol. 2022 Sep;31(3):707\u0026ndash;18. \u003c/li\u003e\n\u003cli\u003eGeetha C, Kumar K, Manjula P, Pavan M. Development and standardisation of the sentenceidentification test in the Kannada language. J Hear Sci. 2014 Mar 31;4(1):18\u0026ndash;26. \u003c/li\u003e\n\u003cli\u003eJagadeesh, A. B., \u0026amp; Uppunda, A. K. Speech-on-Speech Masking: Effect of Maskers with Different Degrees of Linguistic Information. Canadian Journal of Speech-Language Pathology \u0026amp; Audiology, 45(2). 2021; \u003c/li\u003e\n\u003cli\u003eGhosh V, Devananda D, S B H, Kumar H. SPEECH PERCEPTION IN NOISE IN MALAYALAM-SPEAKING YOUNG ADULTS WITH NORMAL HEARING. J Hear Sci. 2024 Aug 1;14(2):33\u0026ndash;8. \u003c/li\u003e\n\u003cli\u003ePena JM, Levi SV. The impact of masker language and talker intelligibility on early bilinguals listeners\u0026rsquo; speech perception. J Acoust Soc Am. 2021 Oct 1;150(4_Supplement):A44\u0026ndash;A44. \u003c/li\u003e\n\u003cli\u003eReetzke R, Lam BPW, Xie Z, Sheng L, Chandrasekaran B. Effect of Simultaneous Bilingualism on Speech Intelligibility across Different Masker Types, Modalities, and Signal-to-Noise Ratios in School-Age Children. Elmer S, editor. PLOS ONE. 2016 Dec 9;11(12):e0168048. \u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"New Language experience, Masking Release, Malayalam, Bilingualism, Cross-linguistic interference","lastPublishedDoi":"10.21203/rs.3.rs-7750783/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7750783/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e\u003cp\u003eSpeech perception in noise is influenced by factors like the type of background noise, the language of the noise, and the listener\u0026rsquo;s linguistic experience. In multilingual contexts like India, understanding how second language learning affects native language speech processing is crucial. Such investigations remain largely unexplored within the Indian multilingual context. The study aimed to explore the effect of new language learning on speech perception with low-frequency native Malayalam phonetically balanced (PB) words in the presence of different masker conditions.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e\u003cp\u003eA cross-sectional study included 54 native Malayalam-speaking participants in two groups: Experienced (n\u0026thinsp;=\u0026thinsp;27; \u0026gt;3 years Kannada exposure) and non-experienced (n\u0026thinsp;=\u0026thinsp;27, no Kannada exposure) participant groups. SNR-50 and masking release were assessed using the Malayalam low-frequency PB words in the presence of 2-talker (2TB) and 4-talker babble (4TB) in Malayalam, Kannada, and speech-shaped noise (SSN).\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e\u003cp\u003eIn both experienced and non-experienced participants, SNR-50 performance followed a 2TB\u0026thinsp;\u0026gt;\u0026thinsp;SSN\u0026thinsp;\u0026gt;\u0026thinsp;4TB trend. Both groups performed better with native (Malayalam) maskers than non-native (Kannada) maskers. Overall, the experienced group outperformed the non-experienced group, though in all masking conditions, differences were not statistically significant. Interestingly, the non-experienced group showed more masking release with the 2TB and non-native (Kannada) masker.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e\u003cp\u003eIncreasing the number of talker complexity (2TB to 4TB) showed decreased performance. Native-language maskers facilitated better speech perception, emphasizing the role of linguistic familiarity. Also, the experienced group demonstrated enhanced speech-noise segregation, improving target speech perception, particularly in complex masking conditions. In contrast, the non-experienced group benefits from reduced cognitive load in simpler auditory environments with non-native maskers.\u003c/p\u003e","manuscriptTitle":"Speech Perception Challenges: The Role of Second Language Experience Across Masker Types in native Malayalam speakers for Low-frequency PB words","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-11-21 18:11:25","doi":"10.21203/rs.3.rs-7750783/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"5c7b82f4-e0b2-4438-a4c8-e04719aeca27","owner":[],"postedDate":"November 21st, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-01-23T14:10:24+00:00","versionOfRecord":[],"versionCreatedAt":"2025-11-21 18:11:25","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7750783","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7750783","identity":"rs-7750783","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}