Development of a Noise Desensitization Program to improve speech perception in noise for adults

Development of a Noise Desensitization Program to improve speech perception in noise for adults | 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 Development of a Noise Desensitization Program to improve speech perception in noise for adults Cathrine Susmitha, Muthu selvi Selvi.T This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8268562/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 8 You are reading this latest preprint version Abstract Background and Objectives: The present study aimed to develop a noise desensitization program for adults to improve speech-in-noise (SPIN) ability and to derive normative scores for this program in adults. Subjects and Methods: The study was carried out in two phases: 1) the development of a noise desensitization program and 2) The administration of the noise desensitization program to normal-hearing individuals to derive normative cut-off scores. In the initial phase, a material containing lists of 50 activities in the Tamil language to improve SPIN using different signal-to-noise ratios (SNR) from 15 dB SNR to -5 dB SNR, including various types of noise such as environmental noise (fan noise), white noise, single speech babble, multi-speech babble, and cafeteria noise for sentences and words, was developed. These recorded materials were administered to a group of 36 normal-hearing individuals within the age range of 18 to 30 years in the second phase. The mean and the confidence interval for each activity were analyzed to derive cut-off scores. Results The overall mean SPIN scores decreased with the reduction of SNR. The mean SPIN scores were more than 90% for up to + 5 dB SNR and reduced to between 80 and 90% at 0 dB SNR; scores further dropped to between 60 and 80% at -5 dB SNR. The participants reported more difficulty with the cafeteria noise, single-speech babble, and multi-speech babble. At the lowest SNRs of 0 dB and − 5 dB, participants perceived sentences easier than words. Obtained mean SPIN scores for each activity was used as cut off scores. This was used as reference to move to the next activity. Conclusion cut-off scores of activities listed in noise desensitization programs vary depending upon the SNR, type of stimuli, and noise in normal hearing population. This indicates the necessary for activity specific normative cut-off to maintain motivation level of participants. Speech perception in SNR noise SNR background noise Tamil Adults Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Introduction Numerous studies have stated that speech understanding ability in noise improves with standardized intervention [ 1 , 2 , 3 , 4 ]. SPIN (SPIN) difficulty is most commonly reported in individuals with hearing loss, auditory processing disorder, auditory neuropathy spectrum disorder, and other neurocognitive conditions like Alzheimer’s and dementia [ 5 ]. Loss of audibility and SPIN difficulty is more common among individuals with hearing impairments. Moreover, individuals with hearing impairments depend highly on their cognitive abilities during SPIN perception [ 6 , 7 , 8 ]. The extent of the role of cognitive load and SPIN scores also varies depending on factors like listening situation, nature of the signal, type, and amount of noise. Auditory training may improve speech in noisy situations and serve as a better rehabilitative technique for hearing aid and cochlear implant users by increasing listening and understanding abilities. Several studies have stated that auditory training, when combined with the use of hearing assistive technologies, has proven to benefit people with listening difficulties. There are also a number of software and training programs that are computer-based, online, and app-based that benefit individuals with SPIN difficulties. With proper training and intervention, speech comprehension also significantly improves [ 2 , 9 , 10 ]. Numerous researchers have demonstrated the effectiveness of a home-based auditory training system [ 11 , 12 ]. Home-based intervention has been more feasible compared to training offered in clinical settings. However, these programs come with a higher cost and require the presence of an audiologist. In other situations, adults with hearing assistive technologies refuse to undergo auditory training due to the potential drawbacks of access to clinics, resource usability issues, social stigma, lack of motivation, and so on. Thus, there is a need for a recorded noise desensitization program that can be accessed via mobile phone at home. Noise desensitization therapy has been proven to improve auditory perception in the presence of background noise in children with APD [ 3 , 4 , 13 , 14 ]. There is a multitude of literature that provides evidence of adult cerebral plasticity following auditory training [ 15 , 16 , 17 ]. These studies exclusively state that the brain undergoes structural and functional changes after a course of auditory training, thus inducing enhanced auditory perception and cognitive functions. A training module should consist of activities that imitate the daily listening environment and adverse listening conditions. Several studies found speech perception in noise varies depending on SNR, noise, and stimulus complexity [ 18 , 19 , 20 , 21 , 22 , 23 ]. Thus, there is a need for programs with varying complexity in signal-to-noise ratio, stimulus, and background noise. The speech perception in noise cut-off score changes based on the above parameters. Therefore, establishing a criterion to determine the minimum scores required to advance to the next level is essential based on the normal hearing population. For accurate implementation of such a program in the adult population, the use of the native language is necessary. Auditory training programs in Tamil have not yet been developed for adults. People across Tamil Nadu speak Tamil, an indigenous language, so the materials developed for this program incorporate the local language, Tamil. Therefore, we can eliminate the language barrier for native Tamil speakers. The current study aims to develop and standardizes a noise desensitization program to improve speech-in-noise ability for Tamil-speaking adults. The study had three objectives. First is to develop the noise desensitization program to improve SPIN for Tamil speaking adults. Second was also report the effect of SNR, type of stimuli and noise on speech perception, finally to derive the normative cuff scores based on SPIN perception in different SNR, noise and stimulus type mentioned in the noise desensitization program. Method Ethics Approval: The study received approval from the Institutional Ethics Committee (REF: CSP/23/SEP/136/816). Informed consent was obtained in their native language (Tamil) from participants prior to data collection. The study was carried out in two phases. Phase I includes the development of the noise desensitization program. This program was administered to individuals with normal hearing sensitivity in phase II. Phase 1: Development of the Noise Desensitization Training Program The activities are adapted from Garstecki’s visual training paradigm [ 24 ]; noise desensitization training [ 4 ]; and the Caregiver/Teacher Administered Remedial Program for the Management of Auditory Processing Disorder [ 25 ]. The activities are arranged in a simple-to-complex manner by manipulating three major parameters: signal-to-noise ratio (SNR), stimulus complexity, and noise complexity. The order of the SNR is changed from + 15 dNSNR to -5 dBSNR. A stimulus of sentences followed by words is used. Noise complexity was arranged from environmental noise (fan noise), white noise, single speech babble, multi-speech babble, and cafeteria noise. SNR of + 15 dB to -5dB was chosen to have positive to negative SNR range which might real life situation. Environmental noise of fan noise was selected as it was common noise which most of people encounter in real life in India. The program contains two parts: 1) Manual and 2) Audio stimuli. Instructions and answer key are mentioned in the manual. Development of the material for the manual Construction of words and sentences Speech material consisting of 300 everyday vocabularies (Bisyallabic words) that were used irrespective of different dialects, education, and background based on the adult’s repertoire is developed in Tamil. In addition, 300 sentences were developed, each containing 3–4 content words with uniform sentence lengths containing a maximum of three to six words per sentence. These sentences are chosen from sources like newspapers, magazines, television news, and everyday-used sentences. Familiarity testing The selected words and sentences were subjected to familiarity testing on 10 individuals who were native Tamil speakers. It was ensured that an equal number of participants from urban and rural areas of Tamil Nadu participated in the familiarity testing to eliminate dialectal variations. Words and sentences that 80% of adults judged more familiar or familiar were selected to construct the final list. Content validation The list of words and sentences that were considered to be most familiar were content validated by five professionals in the fields of Audiology, Speech language pathology, Special education and Tamil professors at the college who were proficient in Tamil language. The expert’s recommendations for usage of words and sentences were taken into consideration. Preparation of the manual Content-validated words and sentences were sorted into groups of activities. A list of activities was developed, containing five lessons with varying signal-to-noise ratios and background noise. Hence, the final manual contained a total of 250 sentences and 250 words with varying signal-to-noise ratios and background noise with increasing stimulus complexity. Each activity includes instructions for both the tester and the individuals involved. Recording of the stimulus The final stimuli were audio recorded with Adobe Audition software (Version 3.0) in a sound treated room. The final stimuli were spoken by a male speaker whose native language was Tamil. The spoken words were picked up by a Logitech boom microphone, which was kept at a distance of 10 cm and positioned at a 0° angle with reference to the face. These sentences and words were recorded in 32-bit resolution at a sampling rate of 44,100. The speaker was instructed to maintain clarity, pace, and effort while reading the material. The recordings were edited in Adobe Audition software. To prevent changes in intensity across the stimuli, the recorded material was normalized. An inter-stimulus interval of 4 seconds was added to maintain uniformity in the stimulus. Goodness rating The final list of stimuli was subjected to Goodness rating by 10 normal-hearing individuals (native Tamil speakers) to check the intelligibility of the stimuli. Stimuli with 100% correct responses were included in the recorded version. The stimuli with incorrect responses were re-recorded and further evaluated by goodness testing. Stimuli that were identified 100% of the time by all individuals with normal hearing sensitivity were included in the final lists. Recording of Masking Noise Adobe Audition 2023 software (version v23.0.0.54) was used for recording and editing the recorded noise stimuli. Environmental noise (fan noise), single speech babble, multi-speech babble and cafeteria noise were recorded. White noise was generated from Adobe Audition. The standardized Tamil passage developed by Subramaniyan (2005) was utilized to record single-speech babble and 4 talker babble. The Tamil passage was given prior to the talkers to get familiar with the content and pronunciation of words. Further, single-talker babble and multi-talker babble were recorded with a Logitech boom microphone placed 10cm away from the talker. Multi-talker babble was recorded from 4 individuals (two male and two female talkers). The independent recordings of 4-talker (2 male and 2 female) were normalized, and the normalized recordings were merged using multitrack in Adobe Audition to create 4 talker babble. The final output of multi-speech babble was obtained in mono mode. The Single talker babble was recorded by asking one adult Tamil-speaking female to read the given standardized Tamil passage. The recording was normalized to derive a final output for single speech babble. Fan noise was recorded for environmental noise. Ceiling fan noise was recorded from a distance of 1 meter from the fan in the ON position in a room. Mixing of Noise and Stimulus In Adobe Audition, in stereo mode, the stimuli were routed to the right channel, and the left channel was allotted for noise. The different types of noises were added at appropriate intervals as per the stimuli list. For example, Activity 1 in the manual comprises environmental noise. Hence, environmental noise (fan noise) was added for each stimulus in the background. The onset of the noise was set to begin prior to the stimulus by 0.5 seconds, and the offset of the noise was delayed for 0.5 seconds after the stimulus ends. It was ensured that, noise is present only when there is a stimulus to avoid adaptation. Creation of Signal-to-noise ratio (SNR) The average root mean square (RMS) amplitude value was utilized to create the signal-to-noise ratio. The amplitude of background noise was adjusted to meet the required SNR for each lesson, ranging from + 15 dBSNR to -5 dBSNR. The RMS amplitude value of the stimulus and noise was adjusted accordingly as per the required speech-to-noise ratio of 15 dB to -5 dB SNR. For example, when the average RMS amplitude of the stimulus was − 30, the average RMS amplitude of the noise was adjusted to -45, respectively, for a signal-to-noise ratio of + 15 dB. Subsequently, through Adobe Audition, the final list was kept in stereo mode, where stimuli were routed to the right channel and noise to the left channel. Table 1 provides activities listed in noise desensitization training. Table 1 List of Activities in noise desensitization training Lessons Activities 1.Speech perception in noise at + 15 dB SNR 1. Words in the presence of environmental noise 2. Sentences in the presence of environmental noise 3. Words in the presence of white noise 4. Sentences in the presence of white noise 5. Words in the presence of multi-speech babble 6. Sentences in the presence of multi-speech babble 7. Words in the presence of single-speech babble 8. Sentences in the presence of single-speech babble 9. Words in the presence of cafeteria noise 10. Sentences in the presence of cafeteria noise 2.Speech perception in noise at + 10dB SNR 1. Words in the presence of environmental noise 2. Sentences in the presence of environmental noise 3. Words in the presence of white noise 4. Sentences in the presence of white noise 5. Words in the presence of multi-speech babble 6. Sentences in the presence of multi-speech babble 7. Words in the presence of single-speech babble 8. Sentences in the presence of single-speech babble 9. Words in the presence of cafeteria noise 10. Sentences in the presence of cafeteria noise 3.Speech perception in noise at + 5 dB SNR 1. Words in the presence of environmental noise 2. Sentences in the presence of environmental noise 3. Words in the presence of white noise 4. Sentences in the presence of white noise 5. Words in the presence of multi-speech babble 6. Sentences in the presence of multi-speech babble 7. Words in the presence of single-speech babble 8. Sentences in the presence of single-speech babble 9. Words in the presence of cafeteria noise 10. Sentences in the presence of cafeteria noise 4.Speech perception in noise at + 0 dB SNR 1. Words in the presence of environmental noise 2. Sentences in the presence of environmental noise 3. Words in the presence of white noise 4. Sentences in the presence of white noise 5. Words in the presence of multi-speech babble 6. Sentences in the presence of multi-speech babble 7. Words in the presence of single-speech babble 8. Sentences in the presence of single-speech babble 9. Words in the presence of cafeteria noise 10. Sentences in the presence of cafeteria noise 5.Speech perception in noise at − 5 dB SNR 1. Words in the presence of environmental noise 2. Sentences in the presence of environmental noise 3. Words in the presence of white noise 4. Sentences in the presence of white noise 5. Words in the presence of multi-speech babble 6. Sentences in the presence of multi-speech babble 7. Words in the presence of single-speech babble 8. Sentences in the presence of single-speech babble 9. Words in the presence of cafeteria noise 10. Sentences in the presence of cafeteria noise Phase 2: Administration of developed Noise desensitization material in normal hearing adults (normative) Sample size calculation The sample size was derived from a similar study [ 1 ]. This sample size was based on an α error of 5% and a power (1-β) of 90% for a one-sample t-test. Based on this value, hypothesize with 90% power and a significance level of 0.05. The sample size required for this study was 36. Power analysis was used to determine the minimum number of participants required to detect a desired effect size with a specified level of confidence and probability (90% for this study). Participants Thirty-six individuals with equal number of both gender with age range of 18 to 30 years (mean age 22 years) who had bilateral normal peripheral hearing were enrolled for the study. We used a calibrated piano inventis audiometer (serial number: AU1CE15102671) to estimate the pure tone threshold and assess speech audiometry testing. The participants were ensured to have a hearing threshold level not exceeding 15 dB for a frequency range of 250 to 8000 Hz at octave frequencies. An otoscopic examination using a handheld otoscope (Welch Allyn 228 series) confirmed all the participants had normal middle ear function. This was supported by immittance findings showing a type ‘A’ tympanogram and the presence of acoustic reflexes at 90–100 dB HL using GSI Grason Stadler Tympstar Pro equipment. All participants had more than 90% speech identification scores in quiet conditions using the PB word list by [ 26 ]. The participants had a pass score on the ‘Screening Checklist for Auditory Processing in Adults’ (SCAP-A) [ 27 ] to rule out any risk for APD. All of them had normal cognitive ability based on informal assessments. None of the participants had articulatory errors or an intellectual disability based on an informal assessment. The participants had Tamil as their native language. Test procedure The participants were made to sit straight in a chair at 0° azimuth from the calibrated loudspeaker (Zebronics 2.1 speaker, model name: ZEB-BT2150RUF) at a distance of 1 meter. The participants were expected to listen to the stimulus spoken in the presence of background noise and repeat the same orally. An open-ended oral response was obtained from the participants. Time breaks were provided in between the testing to maintain the attention of the participants. No form of feedback was provided for the participant’s response. The estimated duration for the completion of audio stimuli listed in the manual was two and a half hours. For an individual, the testing was carried out over a course of 2–3 sessions. Scoring The responses were recorded on a response sheet in the manual. A score of 1 was given if the all listed keywords (given in the manual) in sentences were repeated correctly. All phoneme in the words has to be repeated corrected for score of 1 for activities involving words. The total score obtained from the individuals at each SNR was tabulated in the data sheet. Statistical analysis The mean and 90% confidence interval for each activity in the manual were analyzed using Statistical Package for the Social Sciences (SPSS) software (version 16.0). The results are depicted in data bars for each lesson in the manual. Since the Shapiro-Wilk test indicated that the data followed a normal distribution, a parametric test of MIXED ANOVA to find a main and interaction effect of SNR, noise and stimulus type. Further, one repeated measure ANOVA was done to find the effect of SNR, noise and stimulus type on SPIN scores. Results I. Development of noise scientization program From the selected everyday use of 300 bi syallabic words and 300 sentences with 3 to 4 key words, individuals rated 235 words, 240 sentences are most familiar. Thus, further 50 more words and sentences were added and checked for familiarity. Finally, 270 words, 285 sentences were selected as most familiar. When theses stimuli were given to professional for content validation, The professionals suggested to change words that are not in use in day-to-day use, for example (Paper - காகிதம், Catamaran - கட்டுமரம்). They also recommended removing difficult and unfamiliar vocabularies. They have suggested to change the sentences in colloquial form rather than bookish form. Some sentences which were difficult to recall due to their complexity were removed. Recommendations on altering the sentence length, complexity, and usability were incorporated. 250 words and sentences were finalized for the development phase. Initially recorded 23 words, 16 sentences which did not pass goodness rating were re-recorded and finalized after 100% goodness rating. These selected sentences and words were listed in manual in different SNR, for different types of noise. They were mixed with noise as per the instruction in the manual. II. Effect of SNR, type of noise and stimuli on SPIN scores. Figure 1 to 5 represents the mean speech-in-noise (SPIN) scores with different background noise at different SNR using sentences and words. Repeated measure ANOVA revealed that there is a main and interaction effect of SNR, noise and stimulus type. Main effect of SNR [F(4,140)=575.23,p=0.00] , noise [F(4,140)=109.121,p=0.00], type of stimuli [1,140)=80.16] and interaction effect of SNR and type of noise [F(16,560)=63.117,p=0.00)], SNR and type of stimuli[F(4,140)=7.67,p0.00)], SNR, type of noise and stimuli [F(16,560)=4.97,p=000] were observed. The effect of Signal to Noise Ratio (SNR) on speech perception: Post hoc pairwise comparisons with Bonferroni adjustment indicated no differences in SPIN scores from +15 to +5 dB SNR, whereas a significant decline was observed from +5 dB to -5 dB SNR for fan, white, and single speech noise. For multi-speech babble and cafeteria noise, SPIN scores decreased significantly from +10 dB SNR to -5 dB SNR. The mean SPIN scores above 90% for up to +5 dBSNR. Mean SPIN scores ranged from 80 to 90% at 0 dB SNR, while scores decreased to between 60 and 80% at -5 dB SNR. The effect of noise type on speech perception: Post hoc pairwise comparisons with Bonferroni adjustment indicated that SPIN scores were unaffected by the type of noise across a range from 15 dB to +5 dB SNR. At 0dB and -5dBSNR, SPIN scores exhibit no significant difference between white noise and fan noise, whereas scores are markedly diminished in a comparison with multi-speech babbling, followed by single speech babble and caferteria noise. Cafeteria noise and single speech babble have the lowest score between 60 and 80% at 0 dB SNR and -5 dB SNR. Effect of stimulus type (sentences and words) on speech perception : Based on post hoc pairwise comparison with bonferroni adjustment, type of stimuli with respect to sentences or words have no effects from +15 dB SNR to +5 dB SNR. The mean scores were significantly higher for sentences than words across each activity of noises at 0 dBSNR and -5 dBSNR. The results reveal that the overall mean speech-in-noise scores decreased with a reduction in SNR. The mean speech-in-noise scores decreased with the complexity of noise, from environmental noise (fan noise) to cafeteria noise. The participants reported more difficulty with the cafeteria noise, single-speech babble. Sentences were perceived better compared to words at the lowest SNR of 0 dB and -5 dB SNR. However, no activity had a mean score that was lower than 50% in any of the lessons. III. Determination of normative cut-off based on SPIN scores for noise desensitization training Mean SPIN scores obtained at specific activity were taken as cut off scores. This cut off score was used for two purposes. Initially, activities are arranged in increase in complexity based on earlier literature. Again, the cut off scores were used to check if there is a requirement of rearrangement of activities. SPIN scores were reduced with reduction SNR, increase in spectral and temporal fluctuation of noise (in the order of fan noise, white noise, mutltispeech babble, cafeteria and single speech babble), linguistic redundancy (sentences followed by words). Thus, all listed 50 activities were arranged in this order. Second, the cut off scores were used as references to move to the next activity. The present study found that that the cut-off scores differ depending on SNR, the type of noise and use of stimuli. Discussion Developed noise desentizsation program involves different SNR, noise type and stimulus type to reflect the real word environments (smith et al., 2021). Several studies also reported that training materials should be complex enough to match real life situation. Effect of SNR on speech perception The results of the present study found that the mean speech-in-noise scores decrease with a reduction in signal-to-noise ratio. The mean speech-in-noise scores were more than 90% for up to + 5 dB SNR and reduced between 80 and 90% at 0 dB SNR; scores further dropped between 60 and 80% at -5 dB SNR. The previous studies [ 23 , 28 ] are in support of the current findings that more than 80% scores could be obtained until 5 dBSNR. According to Shojaei et al. [ 28 ], the mean scores at + 10 dBSNR and + 5 dBSNR were 78% and 70%, respectively. Lee et al.,[ 22 ] revealed that the scores reduced from 68% to 32% when the signal-to-noise ratio was decreased from + 5 dBSNR to -5 dBSNR. Previous studies [ 19 , 22 , 29 , 30 ] also reported that speech-in-noise dropped significantly when SNR was reduced to less than 0 dB SNR. Effect of type of stimulus on SPIN perception According to the current study findings, the stimulus variability of either sentences or words had very minimal effects on the SNR up to + 5 dB SNR. The mean scores were higher for sentences than words at 0 dB SNR and − 5 dB SNR. These results are in support of the previous studies [ 31 , 32 ] reported that adults identify sentences better than isolated words with the help of contextual cues and redundancy factors. In addition, Helfer et al.,[33] reported that adults have better cognitive skills and linguistic competency, which pertains to their guessing in adverse listening conditions. Sentences and words have been used as target stimuli for SPIN perception tests by several authors [18, 20, 31, 34,35]. Thus, the present study has used sentences as adults are exposed to sentences in daily listening situations. Furthermore, the present study employs words that remain unaffected by linguistic cues during perception. Effect of type of noise on SPIN scores The present study found that speech-in-noise scores had no effect on the type of noise up to + 5 dB SNR. The participants of the current study reported more difficulty with the cafeteria noise and single-speech babble and multi-speech babble at 0 dB, -5 dB SNR. The present study had a lowest score between 60 and 80% at 0 dB SNR and − 5 dB SNR in the presence of cafeteria noise. Wong & soli [36] also reported low scores when cafeteria noise was present. The authors reasoned that this could be due to the frequency region and the presence of amplitude fluctuations that can effectively mask speech signals. The present study findings with multi-talker and single-talker babble conditions revealed a score of 70–80% at -5 dBSNR. The multi-talker babble noise creates certain challenges for discerning speech as it is more linguistically interfering. The listeners require active attention to separate the target speech from the linguistic background noise. Shukla et al. [ 30 ] in their study, found performance with speech babble noise decreased to 80% at 0 dB SNR. Similarly, Gundmi, Himaja, & Dhamani [ 19 ] revealed a score of 90% at 0 dB SNR. Lee et al., [ 22 ] mentioned that the scores with multi-talker babble noise decreased from 70% at 0 dB SNR to 40% at -5 dB SNR. Need of specific cut off scores for each activity The present study confirms that there is a need to develop normative cut-off scores for each activity listed in the noise desensitization training. This was necessary as SPIN scores differ depending on SNR, type of noise, and nature of stimuli. Earlier studies [ 4 , 14 ] on training SPIN in children with auditory processing disorders have set an arbitrary cut-off ranging from 60–80%. Kumar et al. [ 4 ] have set the uniform cut-off criteria at 75%, irrespective of SNR and stimulus type, based on results on pilot testing on a small group. This may not be possible for the present study, as cut-off scores varied depending on SNR, type of stimuli, and noise. Similar to the present study, the noise desensitization material listed under the caregiver/teacher-administered remedial program for the management of children with auditory processing disorder (CARP-MAP) program for children with auditory processing disorders has a determined cut-off range for individual activities using a normal-hearing children population. The authors found cutoff scores ranging from 70–90% for children aged 7 to 15 years. The authors expected a reduction in speech-in-noise scores for children in their study compared to the present study, likely due to differences in the age group of participants (adults). The cut-off scores in the present study ensure that activities are arranged in increasing complexity. The activities were constructed and arranged according to the difficulty level of the task. It was ensured that the activities were reordered as per the decreasing cut-off scores across lessons in the present study. This is necessary to maintain the individual's attention and motivation. SNR was arranged from + 15 dB SNR to -5 dB SNR as cut-off scores reduced as SNR reduced. The cut-off scores were higher for white noise and environmental noise (fan noise) backgrounds, regardless of the signal-to-noise ratio. The activities with environmental noise (fan noise) and white noise were ordered at the beginning of each lesson, as they were easier to perform for the participants. At -5 dB SNR, the multi-speech babble and single-speech babble backgrounds had slightly lower scores, and the scores further reduced in the cafeteria noise background. Hence, these activities were rearranged at the end of each lesson. The cut-off scores were found to be higher for sentences than words. Thus, activities are given for sentences first, followed by words. Conclusion A noise desensitization program in Tamil has been constructed as a module comprising multiple exercises with varying signal-to-noise ratios, stimuli, and noise types to simulate real-life scenarios. Specific cutoff scores for activities serve as a benchmark for transitioning to subsequent tasks, hence maintaining individual motivation. The developed noise desensitization program can be used as both a home-based and an in-person training initiative for the Tamil-speaking adult population. The efficacy of this program for adults with auditory processing disorder, cochlear implant recipients, and hearing aid users may be explored in future studies. Declarations Funding: There was no funding obtained for this study. Author Contribution Author Contribution:Conceptualization: MuthuSelvi ThangarajData curation: Cathrine SusmithaFormal analysis: MuthuSelvi ThangarajFunding acquisition: -nil Investigation: Cathrine Susmitha Methodology: Cathrine Susmitha, MuthuSelvi ThangarajProject administration: Cathrine SusmithaResources: MuthuSelvi Thangaraj, Cathrine SusmithaSoftware: -Supervision: MuthuSelvi ThangarajValidation: MuthuSelvi ThangarajVisualization Cathrine SusmithaWriting-original draft: Cathrine SusmithaWriting-review & editing: MuthuSelvi ThangarajApproval of final manuscript: MuthuSelvi Thangaraj Data Availability Data can be provided on request. References Barda A, Shapira Y, Fostick L Benefits of Auditory Training with Open-Set Sentences in Babble Noise. Applied Sciences (Basel) [Internet]. 2023 [cited 2025 Dec 3];13(16):9126. 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Available from: https://doi.org/10.1111/ggi.12678 Kant A, Adhyaru M (2009) Home auditory training program (HAP) for cochlear implantees and hearing-impaired children using hearing aids—an outcome of a three-year research project. Indian J Otolaryngol Head Neck Surg [Internet]. [cited 2025 Dec 3];61(1):54–8. Available from: https://doi.org/10.1007/s12070-009-0035-3 Yu P, Kant G, Adhyaru P Neural Correlates of Selective Attention With Hearing Aid Use Followed by ReadMyQuips Auditory Training Program. Ear Hear [Internet]. 2017 Jan-Feb [cited 2025 Dec 3];38(1):28–41. Available from: 10.1097/AUD.0000000000000348 Jutras B, Lafontaine L, East MP, Noël M (2019) Listening in noise training in children with auditory processing disorder: exploring group and individual data. Disabil Rehabil [Internet]. [cited 2025 Dec 3];41(24):2918–26. Available from: https://doi.org/10.1080/09638288.2018.1482377 Kumar P, Singh N, Hussain R (2021) Efficacy of Computer-Based Noise Desensitization Training in Children With Speech-in-Noise Deficits. Am J Audiol [Internet]. [cited 2025 Dec 3];30:1–16. Available from: https://doi.org/10.1044/2021_AJA-20-00153 Anderson S, Kraus N (2013) Auditory Training: Evidence for Neural Plasticity in Older Adults. Perspect Hear Hear Disord Res Diagn [Internet]. [cited 2025 Dec 3];17(1):37. Available from: https://doi.org/10.1044/hhd17.1.37 Kawata NYS, Nouchi R, Oba K, Matsuzaki Y, Kawashima R (2022) Auditory Cognitive Training Improves Brain Plasticity in Healthy Older Adults: Evidence From a Randomized Controlled Trial. Front Aging Neurosci [Internet]. [cited 2025 Dec 3];14. Available from: https://doi.org/10.3389/fnagi.2022.826672 Song JH, Skoe E, Wong PCM, Kraus N Plasticity in the adult human auditory brainstem following short-term linguistic training. J Cogn Neurosci [Internet]. 2008 [cited 2025 Dec 3];20(10):1892–902. Available from: https://doi.org/10.1162/jocn.2008.20131 Gnanasekar S, Vaidyanath R (2019) Perception of Tamil mono-syllabic and bi-syllabic words in multi-talker speech babble by young adults with normal hearing. J Audiol Otol [Internet]. [cited 2025 Dec 3];23(4):181–6. Available from: https://doi.org/10.7874/jao.2018.00465 Gundmi A, Himaja P, Dhamani A (2018) Effectiveness of multitalker babble over speech noise and its implications: A comparative study. Indian J Otol [Internet]. [cited 2025 Dec 3];24(2):88. Available from: https://doi.org/10.4103/indianjotol.indianjotol_24_18 Kalaiah MK, Thomas D, Bhat JS, Ranjan R (2016) Perception of consonants in speech-shaped noise among young and middle-aged adults. J Int Adv Otol [Internet]. [cited 2025 Dec 3];12(2):184–8. Available from: https://doi.org/10.5152/iao.2016.2467 Le Prell CG, Clavier OH (2017) Effects of noise on speech recognition: Challenges for communication by service members. Hear Res [Internet]. [cited 2025 Dec 3];349:76–89. Available from: https://doi.org/10.1016/j.heares.2016.10.004 Lee JY, Lee JT, Heo HJ, Choi CH, Choi SH, Lee K (2015) Speech recognition in real-life background noise by young and middle-aged adults with normal hearing. Korean J Audiol [Internet]. [cited 2025 Dec 3];19(1):39–44. Available from: https://doi.org/10.7874/jao.2015.19.1.39 Shukla B, SPEECH, PERCEPTION PERFORMANCE IN ECOLOGICAL NOISE [Internet] (2022). [cited 2025 Dec 3]. Available from: https://digitalcommons.memphis.edu/etd Garstecki DC (1981) Some effects of training on speech recognition by hearing-impaired adults. Ear Hear [Internet]. 2(5):236–237 Sep-Oct [cited 2025 Dec 3]; Thangaraj M, Seethapathy J, Nagarajan R Efficacy of caregiver/parent administered remedial program for the management of auditory processing disorder. SRU-Gate project [Intramural]. 2009–2010 Menon MS, Thangaraj M, Selvi (2024) Development of a Phonemically Balanced Word List in Tamil for Speech Audiometry and Evaluation of Its Effectiveness in Adults. Indian J Otolaryngol Head Neck Surg [Internet]. [cited 2025 Dec 3];76(1):545–51. Available from: https://doi.org/10.1007/s12070-023-04209-y Vaidyanath R, Yathiraj A (2014) Screening checklist for auditory processing in adults (Scap-a): development and preliminary findings Shojaei E, Ashayeri H, Jafari Z, Reza M, Dast Z, Kamali K Effect of signal-to-noise ratio on the speech perception ability of older adults [Internet]. 2016 [cited 2025 Dec 3]. Available from: http://mjiri.iums.ac.ir Nasiri E, Jalilvand H, Mahdavi ME, Koravand A Auditory Recognition of Words-in-Noise in Normal Hearing and Mild-to-Severe Sensorineural Hearing Loss with Different Configurations. Aud Vestib Res [Internet]. 2024 [cited 2025 Dec 3];33(2):110–7. Available from: https://doi.org/10.18502/avr.v33i2.14813 Shukla B, Rao BS, Saxena U, Verma H (2018) Measurement of SPIN abilities in laboratory and real-world noise. Indian J Otol [Internet]. [cited 2025 Dec 3];24(2):109. Available from: https://doi.org/10.4103/indianjotol.indianjotol_134_17 Humes LE Factors Underlying Individual Differences in Speech-Recognition Threshold (SRT) in Noise Among Older Adults. Front Aging Neurosci [Internet]. 2021 [cited 2025 Dec 3];13. Available from: https://doi.org/10.3389/fnagi.2021.702739 Millman RE, Mattys SL Auditory verbal working memory as a predictor of speech perception in modulated maskers in listeners with normal hearing. J Speech Lang Hear Res Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 10 Apr, 2026 Reviews received at journal 31 Mar, 2026 Reviewers agreed at journal 16 Mar, 2026 Reviewers agreed at journal 29 Jan, 2026 Reviewers invited by journal 29 Jan, 2026 Editor assigned by journal 05 Dec, 2025 Submission checks completed at journal 05 Dec, 2025 First submitted to journal 03 Dec, 2025 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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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8268562","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":582664609,"identity":"f272e63d-8ad5-4091-897e-cf9029d12bca","order_by":0,"name":"Cathrine Susmitha","email":"","orcid":"","institution":"Sri Ramachandra Institute of Higher Education and Research","correspondingAuthor":false,"prefix":"","firstName":"Cathrine","middleName":"","lastName":"Susmitha","suffix":""},{"id":582664610,"identity":"b2d92fac-2a39-47f9-ac7a-b537a8adf082","order_by":1,"name":"Muthu selvi Selvi.T","email":"data:image/png;base64,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","orcid":"","institution":"Sri Ramachandra Institute of Higher Education and Research","correspondingAuthor":true,"prefix":"","firstName":"Muthu","middleName":"selvi","lastName":"Selvi.T","suffix":""}],"badges":[],"createdAt":"2025-12-03 09:38:15","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8268562/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8268562/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":101880556,"identity":"4caca2a6-2c17-44cb-a68d-1275ba7a1520","added_by":"auto","created_at":"2026-02-04 15:03:33","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":45234,"visible":true,"origin":"","legend":"\u003cp\u003eMean, S.D scores of the activities listed in noise desensitization training at +15dBSNR\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-8268562/v1/6386906630c4c66a38ececbe.png"},{"id":101880933,"identity":"b53dfc95-cbdc-451a-9bc7-2668d96246cf","added_by":"auto","created_at":"2026-02-04 15:08:05","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":52757,"visible":true,"origin":"","legend":"\u003cp\u003eMean,S.D scores of the activities listed in noise desensitization training at +10dBSNR\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-8268562/v1/80dbbaf25ae8c36c8d11912f.png"},{"id":101942797,"identity":"00c0641f-2fe0-42d8-9455-21222cf68e0c","added_by":"auto","created_at":"2026-02-05 09:38:14","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":47679,"visible":true,"origin":"","legend":"\u003cp\u003eMean,S.D \u0026nbsp;scores of \u0026nbsp;the activities listed in noise desensitization training at +5dBSNR\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-8268562/v1/28788f9bc72f9a3b591bc008.png"},{"id":101787803,"identity":"928ae003-f425-4003-800f-318ac6ace142","added_by":"auto","created_at":"2026-02-03 15:51:19","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":48920,"visible":true,"origin":"","legend":"\u003cp\u003eMean, S.D scores of the activities listed in noise desensitization training at 0dBSNR\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-8268562/v1/631fe057a837a31a76efa65d.png"},{"id":101787805,"identity":"931e7240-834a-4110-b724-e665bbeb3de2","added_by":"auto","created_at":"2026-02-03 15:51:19","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":48360,"visible":true,"origin":"","legend":"\u003cp\u003eMean, S.D scores of the activities listed in noise desensitization training at -5dBSNR.\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-8268562/v1/4d8d5a7b7a07a85de2f22a19.png"},{"id":101944019,"identity":"2e3cd976-ea1e-4111-b18b-4164e98be804","added_by":"auto","created_at":"2026-02-05 09:47:33","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1059269,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8268562/v1/00ddfb71-387a-4f4e-9ce5-8361a0e251b9.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Development of a Noise Desensitization Program to improve speech perception in noise for adults","fulltext":[{"header":"Introduction","content":"\u003cp\u003eNumerous studies have stated that speech understanding ability in noise improves with standardized intervention [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. SPIN (SPIN) difficulty is most commonly reported in individuals with hearing loss, auditory processing disorder, auditory neuropathy spectrum disorder, and other neurocognitive conditions like Alzheimer\u0026rsquo;s and dementia [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Loss of audibility and SPIN difficulty is more common among individuals with hearing impairments. Moreover, individuals with hearing impairments depend highly on their cognitive abilities during SPIN perception [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. The extent of the role of cognitive load and SPIN scores also varies depending on factors like listening situation, nature of the signal, type, and amount of noise.\u003c/p\u003e \u003cp\u003eAuditory training may improve speech in noisy situations and serve as a better rehabilitative technique for hearing aid and cochlear implant users by increasing listening and understanding abilities. Several studies have stated that auditory training, when combined with the use of hearing assistive technologies, has proven to benefit people with listening difficulties. There are also a number of software and training programs that are computer-based, online, and app-based that benefit individuals with SPIN difficulties. With proper training and intervention, speech comprehension also significantly improves [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Numerous researchers have demonstrated the effectiveness of a home-based auditory training system [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. Home-based intervention has been more feasible compared to training offered in clinical settings.\u003c/p\u003e \u003cp\u003eHowever, these programs come with a higher cost and require the presence of an audiologist. In other situations, adults with hearing assistive technologies refuse to undergo auditory training due to the potential drawbacks of access to clinics, resource usability issues, social stigma, lack of motivation, and so on. Thus, there is a need for a recorded noise desensitization program that can be accessed via mobile phone at home.\u003c/p\u003e \u003cp\u003eNoise desensitization therapy has been proven to improve auditory perception in the presence of background noise in children with APD [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. There is a multitude of literature that provides evidence of adult cerebral plasticity following auditory training [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. These studies exclusively state that the brain undergoes structural and functional changes after a course of auditory training, thus inducing enhanced auditory perception and cognitive functions.\u003c/p\u003e \u003cp\u003eA training module should consist of activities that imitate the daily listening environment and adverse listening conditions. Several studies found speech perception in noise varies depending on SNR, noise, and stimulus complexity [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. Thus, there is a need for programs with varying complexity in signal-to-noise ratio, stimulus, and background noise. The speech perception in noise cut-off score changes based on the above parameters. Therefore, establishing a criterion to determine the minimum scores required to advance to the next level is essential based on the normal hearing population.\u003c/p\u003e \u003cp\u003eFor accurate implementation of such a program in the adult population, the use of the native language is necessary. Auditory training programs in Tamil have not yet been developed for adults. People across Tamil Nadu speak Tamil, an indigenous language, so the materials developed for this program incorporate the local language, Tamil. Therefore, we can eliminate the language barrier for native Tamil speakers.\u003c/p\u003e \u003cp\u003eThe current study aims to develop and standardizes a noise desensitization program to improve speech-in-noise ability for Tamil-speaking adults. The study had three objectives. First is to develop the noise desensitization program to improve SPIN for Tamil speaking adults. Second was also report the effect of SNR, type of stimuli and noise on speech perception, finally to derive the normative cuff scores based on SPIN perception in different SNR, noise and stimulus type mentioned in the noise desensitization program.\u003c/p\u003e"},{"header":"Method","content":"\u003cp\u003e \u003cstrong\u003eEthics Approval:\u003c/strong\u003e \u003cp\u003e The study received approval from the Institutional Ethics Committee (REF: CSP/23/SEP/136/816). Informed consent was obtained in their native language (Tamil) from participants prior to data collection.\u003c/p\u003e \u003c/p\u003e \u003cp\u003eThe study was carried out in two phases. Phase I includes the development of the noise desensitization program. This program was administered to individuals with normal hearing sensitivity in phase II.\u003c/p\u003e \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003ePhase 1: Development of the Noise Desensitization Training Program\u003c/h2\u003e \u003cp\u003eThe activities are adapted from Garstecki\u0026rsquo;s visual training paradigm [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]; noise desensitization training [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]; and the Caregiver/Teacher Administered Remedial Program for the Management of Auditory Processing Disorder [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. The activities are arranged in a simple-to-complex manner by manipulating three major parameters: signal-to-noise ratio (SNR), stimulus complexity, and noise complexity. The order of the SNR is changed from +\u0026thinsp;15 dNSNR to -5 dBSNR. A stimulus of sentences followed by words is used. Noise complexity was arranged from environmental noise (fan noise), white noise, single speech babble, multi-speech babble, and cafeteria noise. SNR of +\u0026thinsp;15 dB to -5dB was chosen to have positive to negative SNR range which might real life situation. Environmental noise of fan noise was selected as it was common noise which most of people encounter in real life in India. The program contains two parts: 1) Manual and 2) Audio stimuli. Instructions and answer key are mentioned in the manual.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eDevelopment of the material for the manual\u003c/h3\u003e\n\u003cp\u003e \u003cstrong\u003eConstruction of words and sentences\u003c/strong\u003e \u003cp\u003eSpeech material consisting of 300 everyday vocabularies (Bisyallabic words) that were used irrespective of different dialects, education, and background based on the adult\u0026rsquo;s repertoire is developed in Tamil. In addition, 300 sentences were developed, each containing 3\u0026ndash;4 content words with uniform sentence lengths containing a maximum of three to six words per sentence. These sentences are chosen from sources like newspapers, magazines, television news, and everyday-used sentences.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eFamiliarity testing\u003c/strong\u003e \u003cp\u003eThe selected words and sentences were subjected to familiarity testing on 10 individuals who were native Tamil speakers. It was ensured that an equal number of participants from urban and rural areas of Tamil Nadu participated in the familiarity testing to eliminate dialectal variations. Words and sentences that 80% of adults judged more familiar or familiar were selected to construct the final list.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eContent validation\u003c/strong\u003e \u003cp\u003eThe list of words and sentences that were considered to be most familiar were content validated by five professionals in the fields of Audiology, Speech language pathology, Special education and Tamil professors at the college who were proficient in Tamil language. The expert\u0026rsquo;s recommendations for usage of words and sentences were taken into consideration.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003ePreparation of the manual\u003c/strong\u003e \u003cp\u003eContent-validated words and sentences were sorted into groups of activities. A list of activities was developed, containing five lessons with varying signal-to-noise ratios and background noise. Hence, the final manual contained a total of 250 sentences and 250 words with varying signal-to-noise ratios and background noise with increasing stimulus complexity. Each activity includes instructions for both the tester and the individuals involved.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eRecording of the stimulus\u003c/strong\u003e \u003cp\u003eThe final stimuli were audio recorded with Adobe Audition software (Version 3.0) in a sound treated room. The final stimuli were spoken by a male speaker whose native language was Tamil. The spoken words were picked up by a Logitech boom microphone, which was kept at a distance of 10 cm and positioned at a 0\u0026deg; angle with reference to the face. These sentences and words were recorded in 32-bit resolution at a sampling rate of 44,100. The speaker was instructed to maintain clarity, pace, and effort while reading the material. The recordings were edited in Adobe Audition software. To prevent changes in intensity across the stimuli, the recorded material was normalized. An inter-stimulus interval of 4 seconds was added to maintain uniformity in the stimulus.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eGoodness rating\u003c/strong\u003e \u003cp\u003eThe final list of stimuli was subjected to Goodness rating by 10 normal-hearing individuals (native Tamil speakers) to check the intelligibility of the stimuli. Stimuli with 100% correct responses were included in the recorded version. The stimuli with incorrect responses were re-recorded and further evaluated by goodness testing. Stimuli that were identified 100% of the time by all individuals with normal hearing sensitivity were included in the final lists.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eRecording of Masking Noise\u003c/strong\u003e \u003cp\u003eAdobe Audition 2023 software (version v23.0.0.54) was used for recording and editing the recorded noise stimuli. Environmental noise (fan noise), single speech babble, multi-speech babble and cafeteria noise were recorded. White noise was generated from Adobe Audition. The standardized Tamil passage developed by Subramaniyan (2005) was utilized to record single-speech babble and 4 talker babble. The Tamil passage was given prior to the talkers to get familiar with the content and pronunciation of words. Further, single-talker babble and multi-talker babble were recorded with a Logitech boom microphone placed 10cm away from the talker. Multi-talker babble was recorded from 4 individuals (two male and two female talkers). The independent recordings of 4-talker (2 male and 2 female) were normalized, and the normalized recordings were merged using multitrack in Adobe Audition to create 4 talker babble. The final output of multi-speech babble was obtained in mono mode. The Single talker babble was recorded by asking one adult Tamil-speaking female to read the given standardized Tamil passage. The recording was normalized to derive a final output for single speech babble. Fan noise was recorded for environmental noise. Ceiling fan noise was recorded from a distance of 1 meter from the fan in the ON position in a room.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eMixing of Noise and Stimulus\u003c/strong\u003e \u003cp\u003eIn Adobe Audition, in stereo mode, the stimuli were routed to the right channel, and the left channel was allotted for noise. The different types of noises were added at appropriate intervals as per the stimuli list. For example, Activity 1 in the manual comprises environmental noise. Hence, environmental noise (fan noise) was added for each stimulus in the background. The onset of the noise was set to begin prior to the stimulus by 0.5 seconds, and the offset of the noise was delayed for 0.5 seconds after the stimulus ends. It was ensured that, noise is present only when there is a stimulus to avoid adaptation.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eCreation of Signal-to-noise ratio (SNR)\u003c/strong\u003e \u003cp\u003eThe average root mean square (RMS) amplitude value was utilized to create the signal-to-noise ratio. The amplitude of background noise was adjusted to meet the required SNR for each lesson, ranging from +\u0026thinsp;15 dBSNR to -5 dBSNR. The RMS amplitude value of the stimulus and noise was adjusted accordingly as per the required speech-to-noise ratio of 15 dB to -5 dB SNR. For example, when the average RMS amplitude of the stimulus was \u0026minus;\u0026thinsp;30, the average RMS amplitude of the noise was adjusted to -45, respectively, for a signal-to-noise ratio of +\u0026thinsp;15 dB. Subsequently, through Adobe Audition, the final list was kept in stereo mode, where stimuli were routed to the right channel and noise to the left channel. Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e provides activities listed in noise desensitization training.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eList of Activities in noise desensitization training\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"2\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLessons\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eActivities\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1.Speech perception in noise at +\u0026thinsp;15 dB SNR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1. Words in the presence of environmental noise\u003c/p\u003e \u003cp\u003e2. Sentences in the presence of environmental noise\u003c/p\u003e \u003cp\u003e3. Words in the presence of white noise\u003c/p\u003e \u003cp\u003e4. Sentences in the presence of white noise\u003c/p\u003e \u003cp\u003e5. Words in the presence of multi-speech babble\u003c/p\u003e \u003cp\u003e6. Sentences in the presence of multi-speech babble\u003c/p\u003e \u003cp\u003e7. Words in the presence of single-speech babble\u003c/p\u003e \u003cp\u003e8. Sentences in the presence of single-speech babble\u003c/p\u003e \u003cp\u003e9. Words in the presence of cafeteria noise\u003c/p\u003e \u003cp\u003e10. Sentences in the presence of cafeteria noise\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2.Speech perception in noise at +\u0026thinsp;10dB SNR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1. Words in the presence of environmental noise\u003c/p\u003e \u003cp\u003e2. Sentences in the presence of environmental noise\u003c/p\u003e \u003cp\u003e3. Words in the presence of white noise\u003c/p\u003e \u003cp\u003e4. Sentences in the presence of white noise\u003c/p\u003e \u003cp\u003e5. Words in the presence of multi-speech babble\u003c/p\u003e \u003cp\u003e6. Sentences in the presence of multi-speech babble\u003c/p\u003e \u003cp\u003e7. Words in the presence of single-speech babble\u003c/p\u003e \u003cp\u003e8. Sentences in the presence of single-speech babble\u003c/p\u003e \u003cp\u003e9. Words in the presence of cafeteria noise\u003c/p\u003e \u003cp\u003e10. Sentences in the presence of cafeteria noise\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3.Speech perception in noise at +\u0026thinsp;5 dB SNR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1. Words in the presence of environmental noise\u003c/p\u003e \u003cp\u003e2. Sentences in the presence of environmental noise\u003c/p\u003e \u003cp\u003e3. Words in the presence of white noise\u003c/p\u003e \u003cp\u003e4. Sentences in the presence of white noise\u003c/p\u003e \u003cp\u003e5. Words in the presence of multi-speech babble\u003c/p\u003e \u003cp\u003e6. Sentences in the presence of multi-speech babble\u003c/p\u003e \u003cp\u003e7. Words in the presence of single-speech babble\u003c/p\u003e \u003cp\u003e8. Sentences in the presence of single-speech babble\u003c/p\u003e \u003cp\u003e9. Words in the presence of cafeteria noise\u003c/p\u003e \u003cp\u003e10. Sentences in the presence of cafeteria noise\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4.Speech perception in noise at +\u0026thinsp;0 dB SNR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1. Words in the presence of environmental noise\u003c/p\u003e \u003cp\u003e2. Sentences in the presence of environmental noise\u003c/p\u003e \u003cp\u003e3. Words in the presence of white noise\u003c/p\u003e \u003cp\u003e4. Sentences in the presence of white noise\u003c/p\u003e \u003cp\u003e5. Words in the presence of multi-speech babble\u003c/p\u003e \u003cp\u003e6. Sentences in the presence of multi-speech babble\u003c/p\u003e \u003cp\u003e7. Words in the presence of single-speech babble\u003c/p\u003e \u003cp\u003e8. Sentences in the presence of single-speech babble\u003c/p\u003e \u003cp\u003e9. Words in the presence of cafeteria noise\u003c/p\u003e \u003cp\u003e10. Sentences in the presence of cafeteria noise\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e5.Speech perception in noise at \u0026minus;\u0026thinsp;5 dB SNR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1. Words in the presence of environmental noise\u003c/p\u003e \u003cp\u003e2. Sentences in the presence of environmental noise\u003c/p\u003e \u003cp\u003e3. Words in the presence of white noise\u003c/p\u003e \u003cp\u003e4. Sentences in the presence of white noise\u003c/p\u003e \u003cp\u003e5. Words in the presence of multi-speech babble\u003c/p\u003e \u003cp\u003e6. Sentences in the presence of multi-speech babble\u003c/p\u003e \u003cp\u003e7. Words in the presence of single-speech babble\u003c/p\u003e \u003cp\u003e8. Sentences in the presence of single-speech babble\u003c/p\u003e \u003cp\u003e9. Words in the presence of cafeteria noise\u003c/p\u003e \u003cp\u003e10. Sentences in the presence of cafeteria noise\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/p\u003e\n\u003ch3\u003ePhase 2: Administration of developed Noise desensitization material in normal hearing adults (normative)\u003c/h3\u003e\n\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eSample size calculation\u003c/h2\u003e \u003cp\u003eThe sample size was derived from a similar study [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. This sample size was based on an α error of 5% and a power (1-β) of 90% for a one-sample t-test. Based on this value, hypothesize with 90% power and a significance level of 0.05. The sample size required for this study was 36. Power analysis was used to determine the minimum number of participants required to detect a desired effect size with a specified level of confidence and probability (90% for this study).\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eParticipants\u003c/h3\u003e\n\u003cp\u003eThirty-six individuals with equal number of both gender with age range of 18 to 30 years (mean age 22 years) who had bilateral normal peripheral hearing were enrolled for the study. We used a calibrated piano inventis audiometer (serial number: AU1CE15102671) to estimate the pure tone threshold and assess speech audiometry testing. The participants were ensured to have a hearing threshold level not exceeding 15 dB for a frequency range of 250 to 8000 Hz at octave frequencies. An otoscopic examination using a handheld otoscope (Welch Allyn 228 series) confirmed all the participants had normal middle ear function. This was supported by immittance findings showing a type \u0026lsquo;A\u0026rsquo; tympanogram and the presence of acoustic reflexes at 90\u0026ndash;100 dB HL using GSI Grason Stadler Tympstar Pro equipment. All participants had more than 90% speech identification scores in quiet conditions using the PB word list by [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. The participants had a pass score on the \u0026lsquo;Screening Checklist for Auditory Processing in Adults\u0026rsquo; (SCAP-A) [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e] to rule out any risk for APD. All of them had normal cognitive ability based on informal assessments. None of the participants had articulatory errors or an intellectual disability based on an informal assessment. The participants had Tamil as their native language.\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eTest procedure\u003c/h2\u003e \u003cp\u003eThe participants were made to sit straight in a chair at 0\u0026deg; azimuth from the calibrated loudspeaker (Zebronics 2.1 speaker, model name: ZEB-BT2150RUF) at a distance of 1 meter. The participants were expected to listen to the stimulus spoken in the presence of background noise and repeat the same orally. An open-ended oral response was obtained from the participants. Time breaks were provided in between the testing to maintain the attention of the participants. No form of feedback was provided for the participant\u0026rsquo;s response. The estimated duration for the completion of audio stimuli listed in the manual was two and a half hours. For an individual, the testing was carried out over a course of 2\u0026ndash;3 sessions.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eScoring\u003c/h3\u003e\n\u003cp\u003eThe responses were recorded on a response sheet in the manual. A score of 1 was given if the all listed keywords (given in the manual) in sentences were repeated correctly. All phoneme in the words has to be repeated corrected for score of 1 for activities involving words. The total score obtained from the individuals at each SNR was tabulated in the data sheet.\u003c/p\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eThe mean and 90% confidence interval for each activity in the manual were analyzed using Statistical Package for the Social Sciences (SPSS) software (version 16.0). The results are depicted in data bars for each lesson in the manual. Since the Shapiro-Wilk test indicated that the data followed a normal distribution, a parametric test of MIXED ANOVA to find a main and interaction effect of SNR, noise and stimulus type. Further, one repeated measure ANOVA was done to find the effect of SNR, noise and stimulus type on SPIN scores.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cstrong\u003eI. \u003cstrong\u003eDevelopment of noise scientization program\u0026nbsp;\u003c/strong\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFrom the selected everyday use of 300 bi syallabic words and 300 sentences with 3 to 4 key words, individuals rated 235 words, 240 sentences are most familiar. Thus, further 50 more words and sentences were added and checked for familiarity. Finally, 270 words, 285 sentences were selected as most familiar. When theses stimuli were given to professional for content validation, The professionals suggested to change words that are not in use in day-to-day use, for example \u003cem\u003e(Paper\u003c/em\u003e-\u003cem\u003eகாகிதம்,\u003c/em\u003e \u003cem\u003eCatamaran\u003c/em\u003e-\u003cem\u003eகட்டுமரம்).\u003c/em\u003e They also recommended removing difficult and unfamiliar vocabularies. They have suggested to change the sentences in colloquial form rather than bookish form. Some sentences which were difficult to recall due to their complexity were removed. Recommendations on altering the sentence length, complexity, and usability were incorporated. 250 words and sentences were finalized for the development phase. Initially recorded 23 words, 16 sentences which did not pass goodness rating were re-recorded and finalized after 100% goodness rating. These selected sentences and words were listed in manual in different SNR, for different types of noise. They were mixed with noise as per the instruction in the manual.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eII. \u003cstrong\u003eEffect of SNR, type of noise and stimuli on SPIN scores.\u0026nbsp;\u003c/strong\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFigure 1 to 5 represents the mean speech-in-noise (SPIN) scores with different background noise at different SNR using sentences and words. Repeated measure ANOVA revealed that there is a main and interaction effect of SNR, noise and stimulus type. Main effect of SNR [F(4,140)=575.23,p=0.00] , noise [F(4,140)=109.121,p=0.00], type of stimuli [1,140)=80.16] and interaction effect of SNR and type of noise [F(16,560)=63.117,p=0.00)], SNR and type of stimuli[F(4,140)=7.67,p0.00)], SNR, type of noise and stimuli [F(16,560)=4.97,p=000] were observed.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eThe effect of Signal to Noise Ratio (SNR) on speech perception:\u003c/strong\u003e Post hoc pairwise comparisons with Bonferroni adjustment indicated no differences in SPIN scores from +15 to +5 dB SNR, whereas a significant decline was observed from +5 dB to -5 dB SNR for fan, white, and single speech noise. For \u0026nbsp;multi-speech babble and cafeteria noise, SPIN scores decreased significantly from +10 dB SNR to -5 dB SNR.\u003c/p\u003e\n\u003cp\u003eThe mean SPIN scores above 90% for up to +5 dBSNR. Mean SPIN scores ranged from 80 to 90% at 0 dB SNR, while scores decreased to between 60 and 80% at -5 dB SNR.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eThe effect of noise type on speech perception:\u003c/strong\u003e Post hoc pairwise comparisons with Bonferroni adjustment indicated that SPIN scores were unaffected by the type of noise across a range from 15 dB to +5 dB SNR. At 0dB and -5dBSNR, SPIN scores exhibit no significant difference between white noise and fan noise, whereas scores are markedly diminished in a comparison with multi-speech babbling, followed by single speech babble and caferteria noise. Cafeteria noise and single speech babble have the lowest score between 60 and 80% at 0 dB SNR and -5 dB SNR.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEffect of stimulus type (sentences and words) on speech perception\u003c/strong\u003e: Based on post hoc pairwise comparison with bonferroni adjustment, type of stimuli with respect to sentences or words have no effects from +15 dB SNR to +5 dB SNR. The mean scores were significantly higher for sentences than words across each activity of noises at 0 dBSNR and -5 dBSNR.\u003c/p\u003e\n\u003cp\u003eThe results reveal that the overall mean speech-in-noise scores decreased with a reduction in SNR. \u0026nbsp;The mean speech-in-noise scores decreased with the complexity of noise, from environmental noise (fan noise) to cafeteria noise. The participants reported more difficulty with the cafeteria noise, single-speech babble. Sentences were perceived better compared to words at the lowest SNR of 0 dB and -5 dB SNR. However, no activity had a mean score that was lower than 50% in any of the lessons.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eIII. \u003cstrong\u003eDetermination of normative cut-off based on SPIN scores for noise desensitization training\u0026nbsp;\u003c/strong\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eMean SPIN scores obtained at specific activity were taken as cut off scores. This cut off score was used for two purposes. Initially, activities are arranged in increase in complexity based on earlier literature. Again, the cut off scores were used to check if there is a requirement of rearrangement of activities. SPIN scores were reduced with reduction SNR, increase in spectral and temporal fluctuation of noise (in the order of fan noise, white noise, mutltispeech babble, cafeteria and single speech babble), linguistic redundancy (sentences followed by words). Thus, all listed 50 activities were arranged in this order. Second, the cut off scores were used as references to move to the next activity. The present study found that that the cut-off scores differ depending on SNR, the type of noise and use of stimuli.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eDeveloped noise desentizsation program involves different SNR, noise type and stimulus type to reflect the real word environments (smith et al., 2021). Several studies also reported that training materials should be complex enough to match real life situation.\u003c/p\u003e \u003cp\u003e \u003cstrong\u003eEffect of SNR on speech perception\u003c/strong\u003e \u003cp\u003eThe results of the present study found that the mean speech-in-noise scores decrease with a reduction in signal-to-noise ratio. The mean speech-in-noise scores were more than 90% for up to +\u0026thinsp;5 dB SNR and reduced between 80 and 90% at 0 dB SNR; scores further dropped between 60 and 80% at -5 dB SNR. The previous studies [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e] are in support of the current findings that more than 80% scores could be obtained until 5 dBSNR. According to Shojaei et al. [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e], the mean scores at +\u0026thinsp;10 dBSNR and +\u0026thinsp;5 dBSNR were 78% and 70%, respectively. Lee et al.,[\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e] revealed that the scores reduced from 68% to 32% when the signal-to-noise ratio was decreased from +\u0026thinsp;5 dBSNR to -5 dBSNR. Previous studies [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e] also reported that speech-in-noise dropped significantly when SNR was reduced to less than 0 dB SNR.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eEffect of type of stimulus on SPIN perception\u003c/strong\u003e \u003cp\u003e According to the current study findings, the stimulus variability of either sentences or words had very minimal effects on the SNR up to +\u0026thinsp;5 dB SNR. The mean scores were higher for sentences than words at 0 dB SNR and \u0026minus;\u0026thinsp;5 dB SNR. These results are in support of the previous studies [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e] reported that adults identify sentences better than isolated words with the help of contextual cues and redundancy factors. In addition, Helfer et al.,[33] reported that adults have better cognitive skills and linguistic competency, which pertains to their guessing in adverse listening conditions. Sentences and words have been used as target stimuli for SPIN perception tests by several authors [18, 20, 31, 34,35]. Thus, the present study has used sentences as adults are exposed to sentences in daily listening situations. Furthermore, the present study employs words that remain unaffected by linguistic cues during perception.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eEffect of type of noise on SPIN scores\u003c/strong\u003e \u003cp\u003eThe present study found that speech-in-noise scores had no effect on the type of noise up to +\u0026thinsp;5 dB SNR. The participants of the current study reported more difficulty with the cafeteria noise and single-speech babble and multi-speech babble at 0 dB, -5 dB SNR. The present study had a lowest score between 60 and 80% at 0 dB SNR and \u0026minus;\u0026thinsp;5 dB SNR in the presence of cafeteria noise. Wong \u0026amp; soli [36] also reported low scores when cafeteria noise was present. The authors reasoned that this could be due to the frequency region and the presence of amplitude fluctuations that can effectively mask speech signals. The present study findings with multi-talker and single-talker babble conditions revealed a score of 70\u0026ndash;80% at -5 dBSNR. The multi-talker babble noise creates certain challenges for discerning speech as it is more linguistically interfering. The listeners require active attention to separate the target speech from the linguistic background noise. Shukla et al. [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e] in their study, found performance with speech babble noise decreased to 80% at 0 dB SNR. Similarly, Gundmi, Himaja, \u0026amp; Dhamani [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e] revealed a score of 90% at 0 dB SNR. Lee et al., [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e] mentioned that the scores with multi-talker babble noise decreased from 70% at 0 dB SNR to 40% at -5 dB SNR.\u003c/p\u003e \u003c/p\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eNeed of specific cut off scores for each activity\u003c/h2\u003e \u003cp\u003eThe present study confirms that there is a need to develop normative cut-off scores for each activity listed in the noise desensitization training. This was necessary as SPIN scores differ depending on SNR, type of noise, and nature of stimuli. Earlier studies [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e] on training SPIN in children with auditory processing disorders have set an arbitrary cut-off ranging from 60\u0026ndash;80%. Kumar et al. [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e] have set the uniform cut-off criteria at 75%, irrespective of SNR and stimulus type, based on results on pilot testing on a small group. This may not be possible for the present study, as cut-off scores varied depending on SNR, type of stimuli, and noise. Similar to the present study, the noise desensitization material listed under the caregiver/teacher-administered remedial program for the management of children with auditory processing disorder (CARP-MAP) program for children with auditory processing disorders has a determined cut-off range for individual activities using a normal-hearing children population. The authors found cutoff scores ranging from 70\u0026ndash;90% for children aged 7 to 15 years. The authors expected a reduction in speech-in-noise scores for children in their study compared to the present study, likely due to differences in the age group of participants (adults).\u003c/p\u003e \u003cp\u003eThe cut-off scores in the present study ensure that activities are arranged in increasing complexity. The activities were constructed and arranged according to the difficulty level of the task. It was ensured that the activities were reordered as per the decreasing cut-off scores across lessons in the present study. This is necessary to maintain the individual's attention and motivation. SNR was arranged from +\u0026thinsp;15 dB SNR to -5 dB SNR as cut-off scores reduced as SNR reduced. The cut-off scores were higher for white noise and environmental noise (fan noise) backgrounds, regardless of the signal-to-noise ratio. The activities with environmental noise (fan noise) and white noise were ordered at the beginning of each lesson, as they were easier to perform for the participants. At -5 dB SNR, the multi-speech babble and single-speech babble backgrounds had slightly lower scores, and the scores further reduced in the cafeteria noise background. Hence, these activities were rearranged at the end of each lesson. The cut-off scores were found to be higher for sentences than words. Thus, activities are given for sentences first, followed by words.\u003c/p\u003e \u003c/div\u003e"},{"header":"Conclusion","content":"\u003cp\u003eA noise desensitization program in Tamil has been constructed as a module comprising multiple exercises with varying signal-to-noise ratios, stimuli, and noise types to simulate real-life scenarios. Specific cutoff scores for activities serve as a benchmark for transitioning to subsequent tasks, hence maintaining individual motivation. The developed noise desensitization program can be used as both a home-based and an in-person training initiative for the Tamil-speaking adult population. The efficacy of this program for adults with auditory processing disorder, cochlear implant recipients, and hearing aid users may be explored in future studies.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eFunding:\u003c/h2\u003e \u003cp\u003eThere was no funding obtained for this study.\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eAuthor Contribution:Conceptualization: MuthuSelvi ThangarajData curation: Cathrine SusmithaFormal analysis: MuthuSelvi ThangarajFunding acquisition: -nil Investigation: Cathrine Susmitha Methodology: Cathrine Susmitha, MuthuSelvi ThangarajProject administration: Cathrine SusmithaResources: MuthuSelvi Thangaraj, Cathrine SusmithaSoftware: -Supervision: MuthuSelvi ThangarajValidation: MuthuSelvi ThangarajVisualization Cathrine SusmithaWriting-original draft: Cathrine SusmithaWriting-review \u0026amp; editing: MuthuSelvi ThangarajApproval of final manuscript: MuthuSelvi Thangaraj\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eData can be provided on request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eBarda A, Shapira Y, Fostick L Benefits of Auditory Training with Open-Set Sentences in Babble Noise. 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Front Aging Neurosci [Internet]. 2021 [cited 2025 Dec 3];13. Available from: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3389/fnagi.2021.702739\u003c/span\u003e\u003cspan address=\"10.3389/fnagi.2021.702739\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMillman RE, Mattys SL Auditory verbal working memory as a predictor of speech perception in modulated maskers in listeners with normal hearing. J Speech Lang Hear Res\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"the-egyptian-journal-of-otolaryngology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"Learn more about [The Egyptian Journal of Otolaryngology](https://ejo.springeropen.com/)","snPcode":"43163","submissionUrl":"https://submission.springernature.com/new-submission/43163/3","title":"The Egyptian Journal of Otolaryngology","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer Open","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Speech perception in SNR noise, SNR, background noise, Tamil, Adults","lastPublishedDoi":"10.21203/rs.3.rs-8268562/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8268562/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground and Objectives:\u003c/h2\u003e \u003cp\u003eThe present study aimed to develop a noise desensitization program for adults to improve speech-in-noise (SPIN) ability and to derive normative scores for this program in adults.\u003c/p\u003e\u003ch2\u003eSubjects and Methods:\u003c/h2\u003e \u003cp\u003eThe study was carried out in two phases: 1) the development of a noise desensitization program and 2) The administration of the noise desensitization program to normal-hearing individuals to derive normative cut-off scores. In the initial phase, a material containing lists of 50 activities in the Tamil language to improve SPIN using different signal-to-noise ratios (SNR) from 15 dB SNR to -5 dB SNR, including various types of noise such as environmental noise (fan noise), white noise, single speech babble, multi-speech babble, and cafeteria noise for sentences and words, was developed. These recorded materials were administered to a group of 36 normal-hearing individuals within the age range of 18 to 30 years in the second phase. The mean and the confidence interval for each activity were analyzed to derive cut-off scores.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eThe overall mean SPIN scores decreased with the reduction of SNR. The mean SPIN scores were more than 90% for up to +\u0026thinsp;5 dB SNR and reduced to between 80 and 90% at 0 dB SNR; scores further dropped to between 60 and 80% at -5 dB SNR. The participants reported more difficulty with the cafeteria noise, single-speech babble, and multi-speech babble. At the lowest SNRs of 0 dB and \u0026minus;\u0026thinsp;5 dB, participants perceived sentences easier than words. Obtained mean SPIN scores for each activity was used as cut off scores. This was used as reference to move to the next activity.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003ecut-off scores of activities listed in noise desensitization programs vary depending upon the SNR, type of stimuli, and noise in normal hearing population. This indicates the necessary for activity specific normative cut-off to maintain motivation level of participants.\u003c/p\u003e","manuscriptTitle":"Development of a Noise Desensitization Program to improve speech perception in noise for adults","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-02-03 15:51:14","doi":"10.21203/rs.3.rs-8268562/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-04-10T19:49:10+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-03-31T06:26:54+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"236272399816744591460682794173992557534","date":"2026-03-16T09:21:21+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"169882435162796518214111180989468473416","date":"2026-01-29T20:00:53+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-01-29T19:57:50+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-12-05T10:03:34+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-12-05T10:03:19+00:00","index":"","fulltext":""},{"type":"submitted","content":"The Egyptian Journal of Otolaryngology","date":"2025-12-03T09:25:42+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"the-egyptian-journal-of-otolaryngology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"Learn more about [The Egyptian Journal of Otolaryngology](https://ejo.springeropen.com/)","snPcode":"43163","submissionUrl":"https://submission.springernature.com/new-submission/43163/3","title":"The Egyptian Journal of Otolaryngology","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer Open","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"093be98e-90d6-4c1b-b565-067c4b5479f3","owner":[],"postedDate":"February 3rd, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-04-19T09:54:46+00:00","versionOfRecord":[],"versionCreatedAt":"2026-02-03 15:51:14","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8268562","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8268562","identity":"rs-8268562","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}