Cognitive Load Associated with Speaking Clearly in Reverberant Rooms

Cognitive Load Associated with Speaking Clearly in Reverberant Rooms | 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 Article Cognitive Load Associated with Speaking Clearly in Reverberant Rooms Keiko Ishikawa, Silvia Murgia, Hannah Li, Elisabeth Renkert, Pasquale Bottalico This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4022395/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 29 Aug, 2024 Read the published version in Scientific Reports → Version 1 posted 12 You are reading this latest preprint version Abstract Communication is a fundamental aspect of human interaction, yet many individuals must speak in less-than-ideal acoustic environments daily. Adapting their speech to ensure intelligibility in these varied settings can impose a significant cognitive burden. Understanding this burden on talkers has significant implications for the design of public spaces and workplace environments, as well as speaker training programs. The aim of this study was to examine how room acoustics and speaking style affect cognitive load through self-rating of mental demand and pupillometry. Nineteen adult native speakers of American English were instructed to read sentences in both casual and clear speech—a technique known to enhance intelligibility—across three levels of reverberation (0.05s, 1.2s, and 1.83s at 500–1000 Hz). Our findings revealed that speaking style consistently affects the cognitive load on talkers more than room acoustics across the tested reverberation range. Specifically, pupillometry data suggested that speaking in clear speech elevates the cognitive load comparably to speaking in a room with long reverberation, challenging the conventional view of clear speech as an ‘easy’ strategy for improving intelligibility. These results underscore the importance of accounting for talkers' cognitive load when optimizing room acoustics and developing speech production training. Biological sciences/Psychology Biological sciences/Psychology/Human behaviour Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 INTRODUCTION From whispering in a carpeted library to speaking in the echoing expanse of a cathedral, people communicate in a variety of room environments. Some of these environments may present challenges to speaking intelligibly due to the smearing effect caused by reverberation. To navigate these adverse conditions, talkers monitor their speech quality and subsequently adjust the way they produce it. This speech modification is an excellent illustration of Lindblom’s H&H (Hyper and Hypo-articulation) model, which posits that talkers modify their speech in response to various communicative demands and constraints, aiming to balance communication efficiency and effort 1 . In situations that present communication barriers, such as a talker having a speech or voice disorder or a communication partner having hearing loss, the necessity to hyperarticulate becomes even more pronounced. The adjustment of the speech production mechanism and monitoring of its output likely require an increased allocation of cognitive resources and attention. While the cognitive load associated with room acoustics has been well-documented in the context of speech perception 2 – 4 , its association with speech production has not been studied as thoroughly. Understanding the cognitive load associated with speech modification and room acoustics would provide valuable insights for speech training programs and architectural design. Effect of Cognitive Load on Speech Production: “Cognitive load” refers to the mental resources required to process information and perform a specific task 5 . The dynamic relationship between cognitive load and speech production is well-documented. For example, Lively et al. investigated the effect of cognitive load on speech production through talkers engaging in a compensatory visual tracking task 6 . They observed changes in speech characteristics, including increased amplitude and variability, decreased spectral tilt, and altered speaking rate. Lively et al. further examined the acoustic adaptations during the visual tracking task for their intelligibility and reported that certain modifications, particularly in amplitude, could enhance speech intelligibility. These intelligibility-enhancing changes led the authors to conclude that their findings are in line with the premise of the H&H model. Similarly, MacPherson showed that cognitive loads, manipulated through a Stroop task in congruent and incongruent conditions, affect variability in speech motor movements 7 . Furthermore, it has been reported that significant interference can arise from concurrent tasks regulated by brain networks in close anatomical proximity 8 , 9 , suggesting that impact of cognitive load on speech production is affected by the nature of secondary tasks. While these studies illustrate how cognitive load influences speech production, the reverse—how speech production may impact cognitive load—remains underexplored, underscoring a unidirectional understanding in our current body of knowledge. Clear Speech: The most well-known speech adaptation for challenging acoustic environments is the Lombard effect. Under this effect, talkers increase vocal effort in noisy environments, leading to changes in speech rate, intensity, and spectral properties 10 , 11 . While this adaptation occurs automatically, talkers can also intentionally change the way they speak in an attempt to enhance the intelligibility of their speech. One common strategy, particularly in challenging listening environments, is clear speech 12 , 13 . The intelligibility benefit brought by clear speech has proven to be valuable in various contexts, such as for hearing-impaired listeners 14 – 18 and second language learners 12 , 13 . The use of clear speech elicits various changes in speech acoustics. Global changes include increased intensity and pitch range 19 . Temporal adjustments include a decreased speech rate, extended vowel duration, more frequent release of stop consonants and word-final consonants, and increased plosive duration 14 , 19 – 21 . Effect of Room Acoustics on Speech Perception and Production: It has been well-demonstrated that room acoustics, especially reverberation, affect speech perception. Increased reverberation and noise levels negatively impact speech intelligibility, requiring greater listening effort, which is the cognitive load listeners allocate to understand the speech 2 – 4 . Reverberation also affects speech production. Bottalico, Graetzer, and Hunter explored how vocal effort is affected by speech style, room acoustics, and short-term vocal fatigue 22 . They recorded 20 talkers reading aloud in various settings—anechoic, semi-reverberant, and reverberant rooms—amid background classroom babble noise. Their findings showed an increase in sound pressure level (SPL) and perceived effort during loud reading, which diminished with the introduction of reflective panels and in environments with longer reverberation times. Capturing listening effort has been a key interest in speech perception research, with pupillometry extensively used to measure cognitive load allocated by listeners in understanding speech 23 , 24 . This technique is regarded as a psychophysiological measure of cognitive load, based on the observation that the pupil’s size changes in response to various cognitive demands. Specifically, pupil dilation tends to increase as cognitive load rises. In contrast, research that examined mental effort, or cognitive load, involved in speaking under various acoustic condition relied on self-reporting 22 , 25 . For instance, Ishikawa, Li, and Coster explored the mental effort involved in speaking casually versus clearly amid background noise 25 . They found that talkers reported a higher mental demand in multi-talker noise compared to reversed multi-talker noise and speech-shaped noise, but they did not report any difference between casual and clear speech. This absence of difference might suggest a limitation of self-reporting in capturing subtle changes in mental effort required for speech modification. Alternatively, it could imply that using clear speech does not significantly increase mental load for the talkers. Previous research has documented the effect of acoustic environments on speech production, as well as cognitive load related to speech perception in various acoustic environments. Understanding the effect of room acoustics and speech modification on cognitive load is crucial in optimizing communication environments and mitigating the effects of challenging room acoustics. Furthermore, this understanding will provide a critical foundation for designing speaker training programs that are effective yet cognitively manageable, ensuring that individuals can improve their communicative abilities without being burdened by the complexity of the training. Despite its significance, little is known about the cognitive load associated with speech modification and room acoustics. Rooted in the H&H model, this study examines the changes in cognitive load in response to different speaking styles and reverberation levels. It is hypothesized that clear speech, which requires intentional changes to enhance intelligibility, increases cognitive load compared to casual speech. Additionally, it is hypothesized that longer reverberation times will also increase cognitive load. Self-reports and pupillometry are used to measure the change in cognitive load. Lastly, the talkers’ ability to use clear speech in given acoustic conditions is evaluated. METHODS Participants: Nineteen adult native speakers of American English (age range: 18–35 years) with no history of speech, language, or hearing disorders participated in this study. All participants provided written informed consent prior to participation and were compensated for their time. The experimental protocols for this study were approved by the Institutional Review Board of the University of Illinois at Urbana-Champaign (#19215). All experiments were performed in accordance with relevant guidelines and regulations. Experimental Design: This study used within-subjects design with speech production style (casual vs. clear) and room acoustics (No Effect, Small Room and Vocal Room) as the independent variables. Acoustic Simulation Procedure: The virtual acoustic environments were simulated using a real-time effect processor (MX400, Lexicon). Acoustics of the virtual environments were characterized with oral-binaural impulse responses (IRs) calculated with the convolution method. A Class 1 microphone (M2211, NTi Audio) was calibrated with a Class 1 Sound Calibrator (NTi Audio) and then placed at a fixed distance of 15 cm from the corner of the mouth speaker of a Head and Torso Simulator (HATS) (45BC KEMAR HATS, GRAS) at a 45-degree angle. An exponential sweep signal (from 100 to 10,000 Hz) was emitted from the mouth speaker of the HATS and captured by the microphone. The captured signal was processed in real-time to add reverberation using the real-time effect processor. The processed signal was sent to the “ears” of the HATS via open-back headphones (HD600, Sennheiser) and the recorded sweep was deconvolved with the emitted sweep inverted on the time axes to obtain the IR. The average T30 measurements for combined 500 Hz and 1k Hz octave bands were determined for the single-wall sound-proof booth and the two simulated environments. The average T30s were 0.05s, 1.2s, and 1.83s at 500–1000 Hz for the sound-proof booth (No Effect), short reverberation (Small Room) and long reverberation (Vocal Room) conditions, respectively. The delay between the real-time signal and processed signal fed back to the headphones was less than 5 ms, which was below the threshold of noticeable difference 26 . Data Collection Procedures: After consent was obtained, the participants underwent a brief training to ensure that they were able to differentially produce casual and clear speech. The training was provided by two graduate students in speech-language pathology and lasted less than 10 minutes. After confirming that participants were able to produce both speech types distinctly, they were taken to a single-wall sound-proof booth, in which the experiment was conducted. The participants were seated in front of a 27-inch computer monitor, which displayed stimuli presentation slides. The stimuli consisted of a set of three slides for each reverberation and speaking style condition, for a total of eighteen sets (3 room conditions x 2 speaking styles x 3 repetitions). The first slide displayed a baseline cross at the center of the screen for 5 seconds. The second slide displayed two lists of 10 sentences from the Hearing in Noise Test (HINT) 27 and Speech in Noise (SPIN) test 28 . The last slide of the set displayed a 20-point scale for rating the degree of the participant’s mental demand, modeled after the NASA Task Load Index scale 29 . (Fig. 1 ) Participants were instructed to read sentences in both casual and clear speech under each of the three reverberation conditions. The order of conditions was counterbalanced across participants to minimize order effects. During the experiment, the participant’s speech was recorded with a Class 1 microphone (M2211, NTi Audio) placed at a fixed distance of 15 cm from the corner of the participant’s mouth at a 45-degree angle. The microphone was calibrated prior to the experiment. The captured audio signal was split into two channels. The first channel was used for direct recording of the participant’s speech. This channel contained the anechoic speech, used for the following analyses. The speech signal was sent to an external soundboard (UH-7000, TASCAM, Teac Corporation), which was connected to a laptop computer (Latitude 7480, Dell). The signal was then recorded using Audacity 3.0.0 (SourceForge) at the sampling rate of 44,100 Hz and bit depth of 16. The second channel was used to send the signal to the real-time effect processor, and the processed signal was then sent to the open-back headphones, through which the participants heard their speech with reverberation. The open-back headphones were used so that participants could hear their direct sound unaltered and the reverberant tail through the headphones. Pupillary changes were captured by an eye tracker (Aurora, Smart Eye), which was positioned at the bottom of the computer monitor. As specified by the manufacturer of the eye tracker and data collection software (iMotions), the distance between the participants and the eye tracker was maintained between 60 and 70 cm. The participants were instructed to minimize their head movement during the experiment. The eye tracker was calibrated using a 9-point calibration array followed by a 4-point validation array, and the pupil size and eye-to-sensor distance were recorded at 60 Hz with the timing of every trial predetermined before the experiment. To prevent any pupillary reactions to changes in ambient light, consistent brightness was maintained throughout the experiment. Pupillometry Data Preprocessing: Pupil measurements during blinks, as indicated by the iMotion software, were removed from the dataset. Based on the method proposed by Hess and Polt 30 , the pupillometry data were preprocessed to obtain normalized, task-related pupil size changes. For each participant and task, the average pupil size during the baseline cross was computed. Subsequently, the average task-related pupil size was calculated for each task (e.g., reading sentences aloud). The ratio of the average task-related pupil size to the average baseline pupil size was then determined to account for individual differences in baseline pupil size. Acoustic Analysis: Participants’ speech production behaviors were acoustically assessed via speech rate (syllables per second) and intensity. Prior to the analyses, trained research staff manually edited the recordings to extract only the speech portions, using a spectrogram to accurately identify the start and end of each speech segment. These segments were then edited out and saved in .wav format. Lastly, intensity and speech rate were obtained using Praat 31 and its associated scripts 32 . Statistical Analyses: A repeated measures ANOVA was conducted to examine the effects of speech production style and room acoustics on subjective rating of mental demand, pupillometry, and acoustic measurements. Post-hoc comparisons were conducted using Bonferroni-adjusted pairwise comparisons. Significance levels were set at p < .05. RESULTS Subjective Rating of Mental Demand: The results of the repeated measures ANOVA revealed significant main effects for both room acoustics ( F (2, 320) = 4.79, p = 0.009, η² = 0.029) and speech production style ( F (1, 320) = 48.93, p < < 0.001, η² = 0.133), indicating that the participants’ self-ratings were influenced by the room acoustics and the speech production style they employed. The post-hoc analysis using pairwise t-tests with Bonferroni correction revealed significant differences in the ratings between casual and clear speech styles across all room conditions, with No Effect room, t (56) = -4.654, p adj < < 0.001; Small Room, t (56) = -4.187, p adj = 0.0001; and Vocal Room, t (56) = -4.018, p adj = 0.0002. In terms of the difference between room conditions, significant differences were found only between No Effect and Small Room, t (56) = -3.394, p adj = 0.004 in the casual speech style. No significant differences were found between No Effect and Vocal Room, t (56) = -2.143, p adj = 0.109, or between Small Room and Vocal Room, t (56) = 0.553, p adj = 1. In the clear speech style, no significant differences were observed between any of the room conditions, with t (56) = -1.555, p adj = 0.378 for No Effect vs. Small Room; t (56) = -2.261, p adj = 0.083 for No Effect vs. Vocal Room; and t (56) = -0.327, p adj = 1 for Small Room vs. Vocal Room. (Fig. 2 ) Pupillometry: The results of the repeated measures ANOVA showed no significant effect of room acoustics on the ratio of pupillary change ( F (2, 320) = 1.217, p = 0.297, partial η² = 0.008). However, there was a significant effect of speech production style on the pupillary response ( F (1, 320) = 12.057, p < 0.001, partial η² = 0.036). The results of the pairwise t-tests with Bonferroni correction for post-hoc analysis revealed significant differences in the pupillary response between casual and clear speech production styles in both No Effect ( t (56) = -2.932, p adj = 0.005) and Small Room ( t (56) = -3.021, p adj = 0.004). There was no significant difference between casual and clear speech production styles in Vocal Room ( t (56) = -0.338, p adj = 0.736). (Fig. 3 ) Speech Rate: Due to equipment failure, the audio recordings from three participants were not captured. Consequently, the analyses were conducted on data from the remaining sixteen participants. The results of the repeated measures ANOVA revealed significant main effects for speech production style ( F (1, 284) = 35.67, p < < 0.001, η² = 0.460), indicating that the speech rate of the clear speech style was significantly slower than that of the casual speech style. However, there was no a significant effect of room acoustics on the speech rate ( F (2, 284) = 0.16, p < 0.583, η² = 0.002). The results of the pairwise t-tests with Bonferroni correction for post-hoc analysis revealed significant differences in the speech rate between casual and clear speech production styles in both the No Effect ( t (48) = 13.6, p adj < < 0.001), Small Room ( t (48) = 14.3, p adj < < 0.001) and Vocal Room ( t (48) = 13.2, p adj < < 0.001). (Fig. 4 ) Speech Intensity: The results of the repeated measures ANOVA revealed significant main effects for both room acoustics ( F (2, 269) = 3.81, p = .023, η² = 0.028) and speech production style ( F (1, 269) = 195.49, p < .001, η² = 0.421). The results of the pairwise t-tests with Bonferroni correction for post-hoc analysis revealed that the intensity was significantly lower in Small Room compared to No Effect, t (47) = 2.71, p adj = .028 for the casual speech style. There were no significant differences between the No Effect and Vocal Room, t (47) = 0.483, p adj = 1, and between Small Room and Vocal Room, t(47) = -2.02, p adj = .149. For the clear speech style, the intensity was significantly lower in the Small Room condition compared to the Vocal Room, t (47) = -2.80, p adj = .022. There were no significant differences in intensity between the No Effect and Small Room conditions, t (47) = 1.95, p adj = .172, and between the No Effect and Vocal Room conditions, t (47) = -0.237, p adj = 1. The tests also revealed that the intensity of the casual speech style was significantly lower than that of the clear speech style across all tested room conditions (No Effect, t(47) = -9.15, p adj < < 0.001; Small Room, t(47) = -7.30, p adj < < 0.001; Vocal Room, t(47) = -7.54, p adj < < .001). (Fig. 5 ) DISCUSSION The fundamental goal of communication is to gain mutual understanding between conversational partners. Achieving this goal becomes more challenging in certain environments, such as in reverberant and noisy rooms, or in situations with communication barriers. Under these conditions, speakers must adjust their speech production and monitor its output to ensure intelligibility is maintained. These adjustments and monitoring processes likely increase the allocation of cognitive resources, yet the extent of resources required for these processes remains poorly understood. To address this knowledge gap, this study examined how speech modification and room acoustics affect a talker’s cognitive load. The results support the hypothesis that cognitive load is elevated when speaking in clear speech compared to casual speech, as evidenced by both the subjective ratings of mental demand and the pupillometry data. Clear speech consistently led to higher ratings of mental demand across all room conditions. Furthermore, pupillometry data revealed that the ratio of pupil diameter change was significantly greater for clear speech than for casual speech in the No Effect and Small Room conditions, suggesting an increased cognitive load for clear speech. For casual speech, the ratio of pupil diameter change was less than 1, indicating that pupils constricted more compared to the baseline. This constriction is believed to be an artifact of the stimuli presentation slide; unlike the baseline condition, which displayed only a single cross in the middle, the speaking trials showed twenty sentences, making the sentence slides brighter and leading to pupil constriction. Despite this, it is important to emphasize that pupils dilated more for clear speech than for casual speech in these conditions. Additionally, the order of room conditions and speaking styles was randomized to control for the effect of slide content, supporting the conclusion that the observed pupillary responses primarily reflect the cognitive load differences between clear and casual speech styles. The elevated cognitive load observed during clear speech production challenges the longstanding perception of clear speech being an “easy” method for enhancing intelligibility, traditionally thought to require minimal training for effective implementation 12 , 14 . Notably, the lack of significant differences in pupil response between casual and clear speech in environments with extended reverberation times suggests that the cognitive effort for clear speech mirrors that of casual speech under such conditions. This finding urges a deeper exploration into the cognitive demands of various speech modification techniques used in voice and speech therapy, potentially revealing even greater cognitive challenges. According to Cognitive Load Theory, cognitive overload can critically hinder learning capabilities 33 . Techniques that impose excessive cognitive demands may not only be difficult for patients to learn but also to apply in real-world scenarios, where multitasking is often necessary. Thus, by optimizing cognitive loads, therapists can facilitate a more effective learning process, increasing the likelihood that patients will successfully integrate and utilize new communication strategies in their daily interactions. The expectation that reverberation would increase cognitive demand due to difficulty monitoring speech appears intuitive, as reverberation can blur speech sounds, making it harder for speakers to hear their own speech accurately and adjust it in real-time. However, the results provide minimal support for the hypothesis that longer reverberation times increase cognitive load. Subjective ratings indicated that room acoustics influenced mental demand, with significant differences found between the No Effect and Small Room conditions for the casual speech style. However, no change in mental demand was reported for clear speech across any room conditions. Moreover, the pupillometry data did not show a significant effect of room acoustics on pupillary responses for either casual or clear speech styles. The absence of the effect of room acoustics on cognitive load does not align with the premises of the H&H model that speakers engage in hyper-articulation as a strategic response to optimize communication. This unexpected result may suggest that conditions with longer reverberation times may have been needed to reveal its effect. To support this assumption, the acoustic examination of speech recordings indicated that the effects of room acoustics on speech production behaviors was minimal. Contrary to our observations, previous literature has demonstrated the influence of acoustic environments on speech and voice production. For instance, Hodoshima, Arai, and Kurisu discovered that speech produced in reverberant conditions was more intelligible than speech in quiet settings 34 . Similarly, research on singers by Bottalico, Łastowiecka, Glasner, & Redman demonstrated that room acoustics significantly influence vibrato rate, extent, and pitch inaccuracy 35 , indicating that singers modify their vocal production in response to different performance spaces. These findings illustrate the adaptive nature of vocal production to acoustic environments. Notably, the study by Hodoshima, Arai, and Kurisu utilized reverberation times of 3.6 and 2.6 seconds 34 —longer than those used in our study. This suggests that exploring room acoustics with longer reverberation times in future research may more effectively reveal their impact on talkers’ cognitive load. To the best of our knowledge, this study is the first to utilize pupillometry to examine the cognitive load associated with speech modification in varying room acoustics. Employing self-reports and pupillometry as dual measures of cognitive load enables us to capture a more comprehensive picture of the mental effort involved in speech production. This approach is widely utilized in cognitive science because it reveals cognitive loads beyond what individuals can detect themselves, offering insights into unconscious cognitive processes 36 . The discrepancy between the subjective ratings and pupillometry data observed in our study may suggest that the cognitive load associated with adjusting to room acoustics is subtle and might be overshadowed by the more pronounced effect of speech production style. Alternatively, this discrepancy might indicate that the talker’s perception of increased effort does not directly translate to a measurable physiological response in terms of pupillary change. Other psychophysiological methods might be more sensitive to the nuanced effects of room acoustics on cognitive load. The observation that cognitive demand for producing clear speech remains constant across varying room acoustic conditions, including environments with long reverberation times, stimulates further inquiry into how speakers manage speech production in challenging acoustic environments. Two potential theories can be offered for the underlying mechanisms. The first, the invariance of cognitive load, suggests that the cognitive effort involved in producing clear speech is stable across different acoustic environments. This theory posits that engaging in clear speech production sets a fixed cognitive load that remains unaffected by changes in room acoustics. The second theory, cognitive prioritization for speech modification, posits that focusing on clear speech minimizes the impact of room acoustics on cognitive demand. While room acoustics might usually influence cognitive load, this theory argues that the deliberate focus on clear speech production can make these acoustic challenges secondary, highlighting a strategic redirection of cognitive resources towards speech clarity over environmental adaptation. Elucidating the underlying mechanisms is crucial for advancing speaker training methods. For instance, training programs could be tailored to either manage cognitive load during speech production or assist individuals in adapting to diverse acoustic environments, depending on their specific needs. Limitations This study, while providing valuable insights into the cognitive demands of speech production in various acoustic environments, has several limitations that warrant consideration. Firstly, the small sample size may limit the generalizability of our findings. Secondly, the reverberation times used in our experimental setups may not have been long enough to fully capture the impact of room acoustics on cognitive load. Additionally, the study’s reliance on pupillometry and self-reports as the sole measures of cognitive load may not encompass all aspects of cognitive effort involved in speech production. While pupillometry provides a valuable objective measure, incorporating other psychophysiological markers could offer a more nuanced understanding of the cognitive processes at play. Finally, the study did not account for individual differences in speech production habits, auditory feedback sensitivity, or previous training in speech modification techniques, all of which could influence how speakers adjust to varying acoustic conditions. Acknowledging these limitations, our findings lay the groundwork for further research aimed at exploring the intricate relationship among cognitive load, speech production, and room acoustics, ultimately guiding the development of more effective communication strategies and therapeutic interventions. CONCLUSION Findings of this study underscore the importance of understanding the cognitive mechanisms underlying speech production and modification in various acoustic environments. Our findings illuminate the complexities of efforts underlying speech modification, challenging assumptions about the cognitive ease of producing clear speech. Specifically, we demonstrated that clear speech imposes a significant cognitive load on talkers, a load that does not markedly fluctuate with changes in room acoustics. This constancy suggests a potential invariance in cognitive load or a cognitive prioritization that renders acoustic challenges secondary when clarity in speech production is the focus. Declarations Acknowledgements: This study was supported by the University of Illinois at Urbana-Champaign Campus Research Board Grant RB22019 awarded to the first author (Ishikawa). Data availability: The data used in this study are not publicly available due to the sensitive nature of the participant information. However, upon request, the corresponding author can provide de-identified data to qualified researchers for the purpose of replication and further analysis. Author contributions: K.I. conceived and designed the study. K.I., P.B., S.M., H.L, and E.R. performed the experiments. H.L. and E.R. assisted with data organization. K.I. analyzed the data. K.I. wrote the manuscript and P.B., H.L., and E.R. revised it. Competing interests: The authors declare no competing interests. Corresponding author: Correspondence should be addressed to Keiko Ishikawa. Funding Statement: This study was supported by the University of Illinois at Urbana-Champaign Campus Research Board Research Grant RB22019 awarded to the first author (Ishikawa). Data Access Statement: The data used in this study are not publicly available due to the sensitive nature of the participant information. Upon request, the corresponding author can provide de-identified data to qualified researchers for the purpose of replication and further analysis. Ethics declarations: The experimental protocols for this study were approved by the Institutional Review Board of the University of Illinois at Urbana-Champaign (#19215). References Lindblom, B. in Speech production and speech modelling 403–439 (Springer, 1990). Peng, Z. E. & Wang, L. M. Listening effort by native and nonnative listeners due to noise, reverberation, and talker foreign accent during English speech perception. Journal of Speech, Language, and Hearing Research 62, 1068–1081 (2019). Prodi, N. & Visentin, C. 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Pupil response as an indication of effortful listening: The influence of sentence intelligibility. Ear and hearing 31, 480–490 (2010). Ishikawa, K., Li, H. & Coster, E. The Effect of Noise on Initiation and Maintenance of Clear Speech and Associated Mental Demand. Journal of Speech, Language, and Hearing Research 66, 4180–4190 (2023). Lezzoum, N., Gagnon, G. & Voix, J. Echo threshold between passive and electro-acoustic transmission paths in digital hearing protection devices. International Journal of Industrial Ergonomics 53, 372–379 (2016). Nilsson, M., Soli, S. D. & Sullivan, J. A. Development of the Hearing in Noise Test for the measurement of speech reception thresholds in quiet and in noise. The Journal of the Acoustical Society of America 95, 1085–1099 (1994). Kalikow, D. N., Stevens, K. N. & Elliott, L. L. Development of a test of speech intelligibility in noise using sentence materials with controlled word predictability. The Journal of the acoustical society of America 61, 1337–1351 (1977). Hart, S. G. & Staveland, L. E. in Advances in psychology Vol. 52 139–183 (Elsevier, 1988). Hess, E. H. & Polt, J. M. Pupil size in relation to mental activity during simple problem-solving. Science 143, 1190–1192 (1964). Boersma, P. & Weenink, D. Praat: doing phonetics by computer [Computer program]. Version 6.0. 37. Retrieved February 3, 2018 (2018). De Jong, N. H. & Wempe, T. Praat script to detect syllable nuclei and measure speech rate automatically. Behavior research methods 41, 385–390 (2009). Sweller, J. in Psychology of learning and motivation Vol. 55 37–76 (Elsevier, 2011). Hodoshima, N., Arai, T. & Kurisu, K. in Proc. International Congress on Acoustics. 3632–3635 (Citeseer). Bottalico, P., Łastowiecka, N., Glasner, J. D. & Redman, Y. G. Singing in different performance spaces: The effect of room acoustics on vibrato and pitch inaccuracy. The Journal of the Acoustical Society of America 151, 4131–4139 (2022). Sirois, S. & Brisson, J. Pupillometry. Wiley Interdisciplinary Reviews: Cognitive Science 5, 679–692 (2014). Additional Declarations No competing interests reported. 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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-4022395","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":281361279,"identity":"bfb54b0a-0e74-490f-9343-175b67f49302","order_by":0,"name":"Keiko Ishikawa","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAx0lEQVRIiWNgGAWjYBACNgYehoMNQAY/kAkEzCRokWwgVgsDUAsjSIvBAWK18PGfPXhwRs1heeMbaWkSDBXWiQ0EHSaRl3Bww7HDhttupB2TYDiTTowWHoODD9huM267kd4mwdh2mAgt/GeAWv7dtt88A6TlHzFaGHIMDm5su524QQLoMMYGYrRIALXM7PufPOPMs2SLhGPpxgS1yPefMf7Y8y3Ntr89zfDGhxprWYJakAGLRAIpykGA+QOpOkbBKBgFo2BkAAAvrkNMnJTDRwAAAABJRU5ErkJggg==","orcid":"","institution":"University of Kentucky","correspondingAuthor":true,"prefix":"","firstName":"Keiko","middleName":"","lastName":"Ishikawa","suffix":""},{"id":281361280,"identity":"f45ec8db-baf5-4399-940c-4b41b0cbc73e","order_by":1,"name":"Silvia Murgia","email":"","orcid":"","institution":"University of Illinois at Urbana-Champaign","correspondingAuthor":false,"prefix":"","firstName":"Silvia","middleName":"","lastName":"Murgia","suffix":""},{"id":281361281,"identity":"5e7adc2c-cf3e-48c3-bd0a-016af50b3c35","order_by":2,"name":"Hannah Li","email":"","orcid":"","institution":"University of Illinois at Urbana-Champaign","correspondingAuthor":false,"prefix":"","firstName":"Hannah","middleName":"","lastName":"Li","suffix":""},{"id":281361282,"identity":"e124f5e1-877e-44da-ab6a-3b86f6482e7b","order_by":3,"name":"Elisabeth Renkert","email":"","orcid":"","institution":"University of Illinois at Urbana-Champaign","correspondingAuthor":false,"prefix":"","firstName":"Elisabeth","middleName":"","lastName":"Renkert","suffix":""},{"id":281361283,"identity":"4ef0b36d-9e09-47e2-a0a4-9c16b318ccd7","order_by":4,"name":"Pasquale Bottalico","email":"","orcid":"","institution":"University of Illinois at Urbana-Champaign","correspondingAuthor":false,"prefix":"","firstName":"Pasquale","middleName":"","lastName":"Bottalico","suffix":""}],"badges":[],"createdAt":"2024-03-06 23:51:05","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4022395/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4022395/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1038/s41598-024-70820-w","type":"published","date":"2024-08-29T15:57:03+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":53198038,"identity":"72412d2e-39da-4008-b19b-234a7e154fcb","added_by":"auto","created_at":"2024-03-21 18:44:02","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":53398,"visible":true,"origin":"","legend":"\u003cp\u003eSchematic description of a trial\u003c/p\u003e","description":"","filename":"Figure1.png","url":"https://assets-eu.researchsquare.com/files/rs-4022395/v1/dfafc3eb86b2e2cc4d300876.png"},{"id":53198039,"identity":"8e7141c9-4b9f-4cf1-a3c7-7a08b20dca21","added_by":"auto","created_at":"2024-03-21 18:44:02","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":52850,"visible":true,"origin":"","legend":"\u003cp\u003eMental demand ratings for different room acoustic conditions and speaking styles. Error bars indicate standard error. Asterisks indicate a pair of conditions that yielded a statistically significant difference.\u003c/p\u003e","description":"","filename":"Figure2.png","url":"https://assets-eu.researchsquare.com/files/rs-4022395/v1/9f101f99fb02ad105a5af4b2.png"},{"id":53198037,"identity":"94adbd07-795d-40fc-b1c0-7902e7e19183","added_by":"auto","created_at":"2024-03-21 18:44:02","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":64804,"visible":true,"origin":"","legend":"\u003cp\u003eRatio of pupil diameter change for different room acoustic conditions and speaking styles. Error bars indicate standard error. Asterisks indicate a pair of conditions that yielded a statistically significant difference.\u003c/p\u003e","description":"","filename":"Figure3.png","url":"https://assets-eu.researchsquare.com/files/rs-4022395/v1/7acff7ea6ba0ea118a1e754d.png"},{"id":53198040,"identity":"0e4361af-2a93-47a2-b911-eb0db416df2c","added_by":"auto","created_at":"2024-03-21 18:44:02","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":57585,"visible":true,"origin":"","legend":"\u003cp\u003eSpeech rate for different room acoustic conditions and speaking styles. Error bars indicate standard error. Asterisks indicate a pair of conditions that yielded a statistically significant difference.\u003c/p\u003e","description":"","filename":"Figure4.png","url":"https://assets-eu.researchsquare.com/files/rs-4022395/v1/d6d377f3c8d64959a1ce4bb2.png"},{"id":53198041,"identity":"084b421f-82cd-4dba-a67a-91b6f22e885f","added_by":"auto","created_at":"2024-03-21 18:44:03","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":51137,"visible":true,"origin":"","legend":"\u003cp\u003eSpeech intensity for different room acoustic conditions and speaking styles. Error bars indicate standard error. Asterisks indicate a pair of conditions that yielded a statistically significant difference.\u003c/p\u003e","description":"","filename":"Figure5.png","url":"https://assets-eu.researchsquare.com/files/rs-4022395/v1/aa4192f2518dee8ff087a33c.png"},{"id":63820812,"identity":"a8902dad-9dc2-4480-acc8-2b147a550eac","added_by":"auto","created_at":"2024-09-02 16:08:55","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":726156,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4022395/v1/9d0148f0-1050-41b0-87c5-5381a0c55ee3.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Cognitive Load Associated with Speaking Clearly in Reverberant Rooms","fulltext":[{"header":"INTRODUCTION","content":"\u003cp\u003eFrom whispering in a carpeted library to speaking in the echoing expanse of a cathedral, people communicate in a variety of room environments. Some of these environments may present challenges to speaking intelligibly due to the smearing effect caused by reverberation. To navigate these adverse conditions, talkers monitor their speech quality and subsequently adjust the way they produce it. This speech modification is an excellent illustration of Lindblom\u0026rsquo;s H\u0026amp;H (Hyper and Hypo-articulation) model, which posits that talkers modify their speech in response to various communicative demands and constraints, aiming to balance communication efficiency and effort\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e. In situations that present communication barriers, such as a talker having a speech or voice disorder or a communication partner having hearing loss, the necessity to hyperarticulate becomes even more pronounced. The adjustment of the speech production mechanism and monitoring of its output likely require an increased allocation of cognitive resources and attention. While the cognitive load associated with room acoustics has been well-documented in the context of speech perception \u003csup\u003e\u003cspan additionalcitationids=\"CR3\" citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e, its association with speech production has not been studied as thoroughly. Understanding the cognitive load associated with speech modification and room acoustics would provide valuable insights for speech training programs and architectural design.\u003c/p\u003e\n\u003ch3\u003eEffect of Cognitive Load on Speech Production:\u003c/h3\u003e\n\u003cp\u003e\u0026ldquo;Cognitive load\u0026rdquo; refers to the mental resources required to process information and perform a specific task\u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e. The dynamic relationship between cognitive load and speech production is well-documented. For example, Lively et al. investigated the effect of cognitive load on speech production through talkers engaging in a compensatory visual tracking task\u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u003c/sup\u003e. They observed changes in speech characteristics, including increased amplitude and variability, decreased spectral tilt, and altered speaking rate. Lively et al. further examined the acoustic adaptations during the visual tracking task for their intelligibility and reported that certain modifications, particularly in amplitude, could enhance speech intelligibility. These intelligibility-enhancing changes led the authors to conclude that their findings are in line with the premise of the H\u0026amp;H model. Similarly, MacPherson showed that cognitive loads, manipulated through a Stroop task in congruent and incongruent conditions, affect variability in speech motor movements\u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e. Furthermore, it has been reported that significant interference can arise from concurrent tasks regulated by brain networks in close anatomical proximity\u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e,\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e, suggesting that impact of cognitive load on speech production is affected by the nature of secondary tasks. While these studies illustrate how cognitive load influences speech production, the reverse\u0026mdash;how speech production may impact cognitive load\u0026mdash;remains underexplored, underscoring a unidirectional understanding in our current body of knowledge.\u003c/p\u003e \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eClear Speech:\u003c/h2\u003e \u003cp\u003eThe most well-known speech adaptation for challenging acoustic environments is the Lombard effect. Under this effect, talkers increase vocal effort in noisy environments, leading to changes in speech rate, intensity, and spectral properties\u003csup\u003e\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e,\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e. While this adaptation occurs automatically, talkers can also intentionally change the way they speak in an attempt to enhance the intelligibility of their speech. One common strategy, particularly in challenging listening environments, is clear speech\u003csup\u003e\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e,\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e. The intelligibility benefit brought by clear speech has proven to be valuable in various contexts, such as for hearing-impaired listeners\u003csup\u003e\u003cspan additionalcitationids=\"CR15 CR16 CR17\" citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003e and second language learners\u003csup\u003e\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e,\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e. The use of clear speech elicits various changes in speech acoustics. Global changes include increased intensity and pitch range\u003csup\u003e\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003e. Temporal adjustments include a decreased speech rate, extended vowel duration, more frequent release of stop consonants and word-final consonants, and increased plosive duration\u003csup\u003e\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e,\u003cspan additionalcitationids=\"CR20\" citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eEffect of Room Acoustics on Speech Perception and Production:\u003c/h3\u003e\n\u003cp\u003eIt has been well-demonstrated that room acoustics, especially reverberation, affect speech perception. Increased reverberation and noise levels negatively impact speech intelligibility, requiring greater listening effort, which is the cognitive load listeners allocate to understand the speech\u003csup\u003e\u003cspan additionalcitationids=\"CR3\" citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e. Reverberation also affects speech production. Bottalico, Graetzer, and Hunter explored how vocal effort is affected by speech style, room acoustics, and short-term vocal fatigue\u003csup\u003e\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u003c/sup\u003e. They recorded 20 talkers reading aloud in various settings\u0026mdash;anechoic, semi-reverberant, and reverberant rooms\u0026mdash;amid background classroom babble noise. Their findings showed an increase in sound pressure level (SPL) and perceived effort during loud reading, which diminished with the introduction of reflective panels and in environments with longer reverberation times.\u003c/p\u003e \u003cp\u003eCapturing listening effort has been a key interest in speech perception research, with pupillometry extensively used to measure cognitive load allocated by listeners in understanding speech\u003csup\u003e\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e,\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u003c/sup\u003e. This technique is regarded as a psychophysiological measure of cognitive load, based on the observation that the pupil\u0026rsquo;s size changes in response to various cognitive demands. Specifically, pupil dilation tends to increase as cognitive load rises. In contrast, research that examined mental effort, or cognitive load, involved in speaking under various acoustic condition relied on self-reporting\u003csup\u003e\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e,\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u003c/sup\u003e. For instance, Ishikawa, Li, and Coster explored the mental effort involved in speaking casually versus clearly amid background noise\u003csup\u003e\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u003c/sup\u003e. They found that talkers reported a higher mental demand in multi-talker noise compared to reversed multi-talker noise and speech-shaped noise, but they did not report any difference between casual and clear speech. This absence of difference might suggest a limitation of self-reporting in capturing subtle changes in mental effort required for speech modification. Alternatively, it could imply that using clear speech does not significantly increase mental load for the talkers.\u003c/p\u003e \u003cp\u003ePrevious research has documented the effect of acoustic environments on speech production, as well as cognitive load related to speech perception in various acoustic environments. Understanding the effect of room acoustics and speech modification on cognitive load is crucial in optimizing communication environments and mitigating the effects of challenging room acoustics. Furthermore, this understanding will provide a critical foundation for designing speaker training programs that are effective yet cognitively manageable, ensuring that individuals can improve their communicative abilities without being burdened by the complexity of the training. Despite its significance, little is known about the cognitive load associated with speech modification and room acoustics. Rooted in the H\u0026amp;H model, this study examines the changes in cognitive load in response to different speaking styles and reverberation levels. It is hypothesized that clear speech, which requires intentional changes to enhance intelligibility, increases cognitive load compared to casual speech. Additionally, it is hypothesized that longer reverberation times will also increase cognitive load. Self-reports and pupillometry are used to measure the change in cognitive load. Lastly, the talkers\u0026rsquo; ability to use clear speech in given acoustic conditions is evaluated.\u003c/p\u003e"},{"header":"METHODS","content":"\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eParticipants:\u003c/h2\u003e \u003cp\u003eNineteen adult native speakers of American English (age range: 18\u0026ndash;35 years) with no history of speech, language, or hearing disorders participated in this study. All participants provided written informed consent prior to participation and were compensated for their time. The experimental protocols for this study were approved by the Institutional Review Board of the University of Illinois at Urbana-Champaign (#19215). All experiments were performed in accordance with relevant guidelines and regulations.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eExperimental Design:\u003c/h2\u003e \u003cp\u003eThis study used within-subjects design with speech production style (casual vs. clear) and room acoustics (No Effect, Small Room and Vocal Room) as the independent variables.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eAcoustic Simulation Procedure:\u003c/h2\u003e \u003cp\u003eThe virtual acoustic environments were simulated using a real-time effect processor (MX400, Lexicon). Acoustics of the virtual environments were characterized with oral-binaural impulse responses (IRs) calculated with the convolution method. A Class 1 microphone (M2211, NTi Audio) was calibrated with a Class 1 Sound Calibrator (NTi Audio) and then placed at a fixed distance of 15 cm from the corner of the mouth speaker of a Head and Torso Simulator (HATS) (45BC KEMAR HATS, GRAS) at a 45-degree angle. An exponential sweep signal (from 100 to 10,000 Hz) was emitted from the mouth speaker of the HATS and captured by the microphone. The captured signal was processed in real-time to add reverberation using the real-time effect processor. The processed signal was sent to the \u0026ldquo;ears\u0026rdquo; of the HATS via open-back headphones (HD600, Sennheiser) and the recorded sweep was deconvolved with the emitted sweep inverted on the time axes to obtain the IR. The average T30 measurements for combined 500 Hz and 1k Hz octave bands were determined for the single-wall sound-proof booth and the two simulated environments. The average T30s were 0.05s, 1.2s, and 1.83s at 500\u0026ndash;1000 Hz for the sound-proof booth (No Effect), short reverberation (Small Room) and long reverberation (Vocal Room) conditions, respectively. The delay between the real-time signal and processed signal fed back to the headphones was less than 5 ms, which was below the threshold of noticeable difference\u003csup\u003e\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003eData Collection Procedures:\u003c/h2\u003e \u003cp\u003e After consent was obtained, the participants underwent a brief training to ensure that they were able to differentially produce casual and clear speech. The training was provided by two graduate students in speech-language pathology and lasted less than 10 minutes. After confirming that participants were able to produce both speech types distinctly, they were taken to a single-wall sound-proof booth, in which the experiment was conducted. The participants were seated in front of a 27-inch computer monitor, which displayed stimuli presentation slides. The stimuli consisted of a set of three slides for each reverberation and speaking style condition, for a total of eighteen sets (3 room conditions x 2 speaking styles x 3 repetitions). The first slide displayed a baseline cross at the center of the screen for 5 seconds. The second slide displayed two lists of 10 sentences from the Hearing in Noise Test (HINT)\u003csup\u003e\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u003c/sup\u003e and Speech in Noise (SPIN) test\u003csup\u003e\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u003c/sup\u003e. The last slide of the set displayed a 20-point scale for rating the degree of the participant\u0026rsquo;s mental demand, modeled after the NASA Task Load Index scale\u003csup\u003e\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u003c/sup\u003e. (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e) Participants were instructed to read sentences in both casual and clear speech under each of the three reverberation conditions. The order of conditions was counterbalanced across participants to minimize order effects.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e During the experiment, the participant\u0026rsquo;s speech was recorded with a Class 1 microphone (M2211, NTi Audio) placed at a fixed distance of 15 cm from the corner of the participant\u0026rsquo;s mouth at a 45-degree angle. The microphone was calibrated prior to the experiment. The captured audio signal was split into two channels. The first channel was used for direct recording of the participant\u0026rsquo;s speech. This channel contained the anechoic speech, used for the following analyses. The speech signal was sent to an external soundboard (UH-7000, TASCAM, Teac Corporation), which was connected to a laptop computer (Latitude 7480, Dell). The signal was then recorded using Audacity 3.0.0 (SourceForge) at the sampling rate of 44,100 Hz and bit depth of 16. The second channel was used to send the signal to the real-time effect processor, and the processed signal was then sent to the open-back headphones, through which the participants heard their speech with reverberation. The open-back headphones were used so that participants could hear their direct sound unaltered and the reverberant tail through the headphones.\u003c/p\u003e \u003cp\u003ePupillary changes were captured by an eye tracker (Aurora, Smart Eye), which was positioned at the bottom of the computer monitor. As specified by the manufacturer of the eye tracker and data collection software (iMotions), the distance between the participants and the eye tracker was maintained between 60 and 70 cm. The participants were instructed to minimize their head movement during the experiment. The eye tracker was calibrated using a 9-point calibration array followed by a 4-point validation array, and the pupil size and eye-to-sensor distance were recorded at 60 Hz with the timing of every trial predetermined before the experiment. To prevent any pupillary reactions to changes in ambient light, consistent brightness was maintained throughout the experiment.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003ePupillometry Data Preprocessing:\u003c/h2\u003e \u003cp\u003ePupil measurements during blinks, as indicated by the iMotion software, were removed from the dataset. Based on the method proposed by Hess and Polt\u003csup\u003e\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e\u003c/sup\u003e, the pupillometry data were preprocessed to obtain normalized, task-related pupil size changes. For each participant and task, the average pupil size during the baseline cross was computed. Subsequently, the average task-related pupil size was calculated for each task (e.g., reading sentences aloud). The ratio of the average task-related pupil size to the average baseline pupil size was then determined to account for individual differences in baseline pupil size.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eAcoustic Analysis:\u003c/h2\u003e \u003cp\u003e Participants\u0026rsquo; speech production behaviors were acoustically assessed via speech rate (syllables per second) and intensity. Prior to the analyses, trained research staff manually edited the recordings to extract only the speech portions, using a spectrogram to accurately identify the start and end of each speech segment. These segments were then edited out and saved in .wav format. Lastly, intensity and speech rate were obtained using Praat\u003csup\u003e\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u003c/sup\u003e and its associated scripts\u003csup\u003e\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eStatistical Analyses:\u003c/h2\u003e \u003cp\u003eA repeated measures ANOVA was conducted to examine the effects of speech production style and room acoustics on subjective rating of mental demand, pupillometry, and acoustic measurements. Post-hoc comparisons were conducted using Bonferroni-adjusted pairwise comparisons. Significance levels were set at \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.05.\u003c/p\u003e \u003c/div\u003e"},{"header":"RESULTS","content":"\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eSubjective Rating of Mental Demand:\u003c/h2\u003e \u003cp\u003eThe results of the repeated measures ANOVA revealed significant main effects for both room acoustics (\u003cem\u003eF\u003c/em\u003e(2, 320)\u0026thinsp;=\u0026thinsp;4.79, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.009, \u003cem\u003eη\u0026sup2;\u003c/em\u003e = 0.029) and speech production style (\u003cem\u003eF\u003c/em\u003e(1, 320)\u0026thinsp;=\u0026thinsp;48.93, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;\u0026lt;\u0026thinsp;0.001, \u003cem\u003eη\u0026sup2;\u003c/em\u003e = 0.133), indicating that the participants\u0026rsquo; self-ratings were influenced by the room acoustics and the speech production style they employed.\u003c/p\u003e \u003cp\u003eThe post-hoc analysis using pairwise t-tests with Bonferroni correction revealed significant differences in the ratings between casual and clear speech styles across all room conditions, with No Effect room, \u003cem\u003et\u003c/em\u003e(56) = -4.654, \u003cem\u003ep\u003c/em\u003e\u003csub\u003e\u003cem\u003eadj\u003c/em\u003e\u003c/sub\u003e\u0026thinsp;\u0026lt;\u0026thinsp;\u0026lt;\u0026thinsp;0.001; Small Room, \u003cem\u003et\u003c/em\u003e(56) = -4.187, \u003cem\u003ep\u003c/em\u003e\u003csub\u003e\u003cem\u003eadj\u003c/em\u003e\u003c/sub\u003e = 0.0001; and Vocal Room, \u003cem\u003et\u003c/em\u003e(56) = -4.018, \u003cem\u003ep\u003c/em\u003e\u003csub\u003e\u003cem\u003eadj\u003c/em\u003e\u003c/sub\u003e = 0.0002. In terms of the difference between room conditions, significant differences were found only between No Effect and Small Room, \u003cem\u003et\u003c/em\u003e(56) = -3.394, \u003cem\u003ep\u003c/em\u003e\u003csub\u003e\u003cem\u003eadj\u003c/em\u003e\u003c/sub\u003e = 0.004 in the casual speech style. No significant differences were found between No Effect and Vocal Room, \u003cem\u003et\u003c/em\u003e(56) = -2.143, \u003cem\u003ep\u003c/em\u003e\u003csub\u003e\u003cem\u003eadj\u003c/em\u003e\u003c/sub\u003e = 0.109, or between Small Room and Vocal Room, \u003cem\u003et\u003c/em\u003e(56)\u0026thinsp;=\u0026thinsp;0.553, \u003cem\u003ep\u003c/em\u003e\u003csub\u003e\u003cem\u003eadj\u003c/em\u003e\u003c/sub\u003e = 1. In the clear speech style, no significant differences were observed between any of the room conditions, with \u003cem\u003et\u003c/em\u003e(56) = -1.555, \u003cem\u003ep\u003c/em\u003e\u003csub\u003e\u003cem\u003eadj\u003c/em\u003e\u003c/sub\u003e = 0.378 for No Effect vs. Small Room; \u003cem\u003et\u003c/em\u003e(56) = -2.261, \u003cem\u003ep\u003c/em\u003e\u003csub\u003e\u003cem\u003eadj\u003c/em\u003e\u003c/sub\u003e = 0.083 for No Effect vs. Vocal Room; and \u003cem\u003et\u003c/em\u003e(56) = -0.327, \u003cem\u003ep\u003c/em\u003e\u003csub\u003e\u003cem\u003eadj\u003c/em\u003e\u003c/sub\u003e = 1 for Small Room vs. Vocal Room. (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e)\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003ePupillometry:\u003c/h2\u003e \u003cp\u003eThe results of the repeated measures ANOVA showed no significant effect of room acoustics on the ratio of pupillary change (\u003cem\u003eF\u003c/em\u003e(2, 320)\u0026thinsp;=\u0026thinsp;1.217, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.297, partial \u003cem\u003eη\u0026sup2;\u003c/em\u003e = 0.008). However, there was a significant effect of speech production style on the pupillary response (\u003cem\u003eF\u003c/em\u003e(1, 320)\u0026thinsp;=\u0026thinsp;12.057, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001, partial \u003cem\u003eη\u0026sup2;\u003c/em\u003e = 0.036).\u003c/p\u003e \u003cp\u003eThe results of the pairwise t-tests with Bonferroni correction for post-hoc analysis revealed significant differences in the pupillary response between casual and clear speech production styles in both No Effect (\u003cem\u003et\u003c/em\u003e(56) = -2.932, \u003cem\u003ep\u003c/em\u003e\u003csub\u003e\u003cem\u003eadj\u003c/em\u003e\u003c/sub\u003e = 0.005) and Small Room (\u003cem\u003et\u003c/em\u003e(56) = -3.021, \u003cem\u003ep\u003c/em\u003e\u003csub\u003e\u003cem\u003eadj\u003c/em\u003e\u003c/sub\u003e = 0.004). There was no significant difference between casual and clear speech production styles in Vocal Room (\u003cem\u003et\u003c/em\u003e(56) = -0.338, \u003cem\u003ep\u003c/em\u003e\u003csub\u003e\u003cem\u003eadj\u003c/em\u003e\u003c/sub\u003e = 0.736). (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e)\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003eSpeech Rate:\u003c/h2\u003e \u003cp\u003eDue to equipment failure, the audio recordings from three participants were not captured. Consequently, the analyses were conducted on data from the remaining sixteen participants. The results of the repeated measures ANOVA revealed significant main effects for speech production style (\u003cem\u003eF\u003c/em\u003e(1, 284)\u0026thinsp;=\u0026thinsp;35.67, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;\u0026lt;\u0026thinsp;0.001, \u003cem\u003eη\u0026sup2;\u003c/em\u003e = 0.460), indicating that the speech rate of the clear speech style was significantly slower than that of the casual speech style. However, there was no a significant effect of room acoustics on the speech rate (\u003cem\u003eF\u003c/em\u003e(2, 284)\u0026thinsp;=\u0026thinsp;0.16, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.583, \u003cem\u003eη\u0026sup2;\u003c/em\u003e = 0.002).\u003c/p\u003e \u003cp\u003eThe results of the pairwise t-tests with Bonferroni correction for post-hoc analysis revealed significant differences in the speech rate between casual and clear speech production styles in both the No Effect (\u003cem\u003et\u003c/em\u003e(48)\u0026thinsp;=\u0026thinsp;13.6, \u003cem\u003ep\u003c/em\u003e\u003csub\u003e\u003cem\u003eadj\u003c/em\u003e\u003c/sub\u003e\u0026thinsp;\u0026lt;\u0026thinsp;\u0026lt;\u0026thinsp;0.001), Small Room (\u003cem\u003et\u003c/em\u003e(48)\u0026thinsp;=\u0026thinsp;14.3, \u003cem\u003ep\u003c/em\u003e\u003csub\u003e\u003cem\u003eadj\u003c/em\u003e\u003c/sub\u003e\u0026thinsp;\u0026lt;\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and Vocal Room (\u003cem\u003et\u003c/em\u003e(48)\u0026thinsp;=\u0026thinsp;13.2, \u003cem\u003ep\u003c/em\u003e\u003csub\u003e\u003cem\u003eadj\u003c/em\u003e\u003c/sub\u003e\u0026thinsp;\u0026lt;\u0026thinsp;\u0026lt;\u0026thinsp;0.001). (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e)\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003eSpeech Intensity:\u003c/h2\u003e \u003cp\u003eThe results of the repeated measures ANOVA revealed significant main effects for both room acoustics (\u003cem\u003eF\u003c/em\u003e(2, 269)\u0026thinsp;=\u0026thinsp;3.81, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.023, \u003cem\u003eη\u0026sup2;\u003c/em\u003e = 0.028) and speech production style (\u003cem\u003eF\u003c/em\u003e(1, 269)\u0026thinsp;=\u0026thinsp;195.49, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.001, \u003cem\u003eη\u0026sup2;\u003c/em\u003e = 0.421).\u003c/p\u003e \u003cp\u003eThe results of the pairwise t-tests with Bonferroni correction for post-hoc analysis revealed that the intensity was significantly lower in Small Room compared to No Effect, \u003cem\u003et\u003c/em\u003e(47)\u0026thinsp;=\u0026thinsp;2.71, \u003cem\u003ep\u003c/em\u003e\u003csub\u003e\u003cem\u003eadj\u003c/em\u003e\u003c/sub\u003e = .028 for the casual speech style. There were no significant differences between the No Effect and Vocal Room, \u003cem\u003et\u003c/em\u003e(47)\u0026thinsp;=\u0026thinsp;0.483, \u003cem\u003ep\u003c/em\u003e\u003csub\u003e\u003cem\u003eadj\u003c/em\u003e\u003c/sub\u003e = 1, and between Small Room and Vocal Room, t(47) = -2.02, \u003cem\u003ep\u003c/em\u003e\u003csub\u003e\u003cem\u003eadj\u003c/em\u003e\u003c/sub\u003e = .149. For the clear speech style, the intensity was significantly lower in the Small Room condition compared to the Vocal Room, \u003cem\u003et\u003c/em\u003e(47) = -2.80, \u003cem\u003ep\u003c/em\u003e\u003csub\u003e\u003cem\u003eadj\u003c/em\u003e\u003c/sub\u003e = .022. There were no significant differences in intensity between the No Effect and Small Room conditions, \u003cem\u003et\u003c/em\u003e(47)\u0026thinsp;=\u0026thinsp;1.95, \u003cem\u003ep\u003c/em\u003e\u003csub\u003e\u003cem\u003eadj\u003c/em\u003e\u003c/sub\u003e = .172, and between the No Effect and Vocal Room conditions, \u003cem\u003et\u003c/em\u003e(47) = -0.237, \u003cem\u003ep\u003c/em\u003e\u003csub\u003e\u003cem\u003eadj\u003c/em\u003e\u003c/sub\u003e = 1.\u003c/p\u003e \u003cp\u003eThe tests also revealed that the intensity of the casual speech style was significantly lower than that of the clear speech style across all tested room conditions (No Effect, t(47) = -9.15, \u003cem\u003ep\u003c/em\u003e\u003csub\u003e\u003cem\u003eadj\u003c/em\u003e\u003c/sub\u003e\u0026thinsp;\u0026lt;\u0026thinsp;\u0026lt;\u0026thinsp;0.001; Small Room, t(47) = -7.30, \u003cem\u003ep\u003c/em\u003e\u003csub\u003e\u003cem\u003eadj\u003c/em\u003e\u003c/sub\u003e\u0026thinsp;\u0026lt;\u0026thinsp;\u0026lt;\u0026thinsp;0.001; Vocal Room, t(47) = -7.54, \u003cem\u003ep\u003c/em\u003e\u003csub\u003e\u003cem\u003eadj\u003c/em\u003e\u003c/sub\u003e\u0026thinsp;\u0026lt;\u0026thinsp;\u0026lt;\u0026thinsp;.001). (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e)\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"DISCUSSION","content":"\u003cp\u003eThe fundamental goal of communication is to gain mutual understanding between conversational partners. Achieving this goal becomes more challenging in certain environments, such as in reverberant and noisy rooms, or in situations with communication barriers. Under these conditions, speakers must adjust their speech production and monitor its output to ensure intelligibility is maintained. These adjustments and monitoring processes likely increase the allocation of cognitive resources, yet the extent of resources required for these processes remains poorly understood. To address this knowledge gap, this study examined how speech modification and room acoustics affect a talker\u0026rsquo;s cognitive load.\u003c/p\u003e \u003cp\u003eThe results support the hypothesis that cognitive load is elevated when speaking in clear speech compared to casual speech, as evidenced by both the subjective ratings of mental demand and the pupillometry data. Clear speech consistently led to higher ratings of mental demand across all room conditions. Furthermore, pupillometry data revealed that the ratio of pupil diameter change was significantly greater for clear speech than for casual speech in the No Effect and Small Room conditions, suggesting an increased cognitive load for clear speech. For casual speech, the ratio of pupil diameter change was less than 1, indicating that pupils constricted more compared to the baseline. This constriction is believed to be an artifact of the stimuli presentation slide; unlike the baseline condition, which displayed only a single cross in the middle, the speaking trials showed twenty sentences, making the sentence slides brighter and leading to pupil constriction. Despite this, it is important to emphasize that pupils dilated more for clear speech than for casual speech in these conditions. Additionally, the order of room conditions and speaking styles was randomized to control for the effect of slide content, supporting the conclusion that the observed pupillary responses primarily reflect the cognitive load differences between clear and casual speech styles.\u003c/p\u003e \u003cp\u003eThe elevated cognitive load observed during clear speech production challenges the longstanding perception of clear speech being an \u0026ldquo;easy\u0026rdquo; method for enhancing intelligibility, traditionally thought to require minimal training for effective implementation\u003csup\u003e\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e,\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e. Notably, the lack of significant differences in pupil response between casual and clear speech in environments with extended reverberation times suggests that the cognitive effort for clear speech mirrors that of casual speech under such conditions. This finding urges a deeper exploration into the cognitive demands of various speech modification techniques used in voice and speech therapy, potentially revealing even greater cognitive challenges. According to Cognitive Load Theory, cognitive overload can critically hinder learning capabilities\u003csup\u003e\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e\u003c/sup\u003e. Techniques that impose excessive cognitive demands may not only be difficult for patients to learn but also to apply in real-world scenarios, where multitasking is often necessary. Thus, by optimizing cognitive loads, therapists can facilitate a more effective learning process, increasing the likelihood that patients will successfully integrate and utilize new communication strategies in their daily interactions.\u003c/p\u003e \u003cp\u003eThe expectation that reverberation would increase cognitive demand due to difficulty monitoring speech appears intuitive, as reverberation can blur speech sounds, making it harder for speakers to hear their own speech accurately and adjust it in real-time. However, the results provide minimal support for the hypothesis that longer reverberation times increase cognitive load. Subjective ratings indicated that room acoustics influenced mental demand, with significant differences found between the No Effect and Small Room conditions for the casual speech style. However, no change in mental demand was reported for clear speech across any room conditions. Moreover, the pupillometry data did not show a significant effect of room acoustics on pupillary responses for either casual or clear speech styles. The absence of the effect of room acoustics on cognitive load does not align with the premises of the H\u0026amp;H model that speakers engage in hyper-articulation as a strategic response to optimize communication. This unexpected result may suggest that conditions with longer reverberation times may have been needed to reveal its effect. To support this assumption, the acoustic examination of speech recordings indicated that the effects of room acoustics on speech production behaviors was minimal.\u003c/p\u003e \u003cp\u003eContrary to our observations, previous literature has demonstrated the influence of acoustic environments on speech and voice production. For instance, Hodoshima, Arai, and Kurisu discovered that speech produced in reverberant conditions was more intelligible than speech in quiet settings\u003csup\u003e\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e\u003c/sup\u003e. Similarly, research on singers by Bottalico, Łastowiecka, Glasner, \u0026amp; Redman demonstrated that room acoustics significantly influence vibrato rate, extent, and pitch inaccuracy\u003csup\u003e\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e\u003c/sup\u003e, indicating that singers modify their vocal production in response to different performance spaces. These findings illustrate the adaptive nature of vocal production to acoustic environments. Notably, the study by Hodoshima, Arai, and Kurisu utilized reverberation times of 3.6 and 2.6 seconds\u003csup\u003e\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e\u003c/sup\u003e\u0026mdash;longer than those used in our study. This suggests that exploring room acoustics with longer reverberation times in future research may more effectively reveal their impact on talkers\u0026rsquo; cognitive load.\u003c/p\u003e \u003cp\u003eTo the best of our knowledge, this study is the first to utilize pupillometry to examine the cognitive load associated with speech modification in varying room acoustics. Employing self-reports and pupillometry as dual measures of cognitive load enables us to capture a more comprehensive picture of the mental effort involved in speech production. This approach is widely utilized in cognitive science because it reveals cognitive loads beyond what individuals can detect themselves, offering insights into unconscious cognitive processes\u003csup\u003e\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e\u003c/sup\u003e. The discrepancy between the subjective ratings and pupillometry data observed in our study may suggest that the cognitive load associated with adjusting to room acoustics is subtle and might be overshadowed by the more pronounced effect of speech production style. Alternatively, this discrepancy might indicate that the talker\u0026rsquo;s perception of increased effort does not directly translate to a measurable physiological response in terms of pupillary change. Other psychophysiological methods might be more sensitive to the nuanced effects of room acoustics on cognitive load.\u003c/p\u003e \u003cp\u003eThe observation that cognitive demand for producing clear speech remains constant across varying room acoustic conditions, including environments with long reverberation times, stimulates further inquiry into how speakers manage speech production in challenging acoustic environments. Two potential theories can be offered for the underlying mechanisms. The first, the invariance of cognitive load, suggests that the cognitive effort involved in producing clear speech is stable across different acoustic environments. This theory posits that engaging in clear speech production sets a fixed cognitive load that remains unaffected by changes in room acoustics. The second theory, cognitive prioritization for speech modification, posits that focusing on clear speech minimizes the impact of room acoustics on cognitive demand. While room acoustics might usually influence cognitive load, this theory argues that the deliberate focus on clear speech production can make these acoustic challenges secondary, highlighting a strategic redirection of cognitive resources towards speech clarity over environmental adaptation. Elucidating the underlying mechanisms is crucial for advancing speaker training methods. For instance, training programs could be tailored to either manage cognitive load during speech production or assist individuals in adapting to diverse acoustic environments, depending on their specific needs.\u003c/p\u003e \u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003eLimitations\u003c/h2\u003e \u003cp\u003eThis study, while providing valuable insights into the cognitive demands of speech production in various acoustic environments, has several limitations that warrant consideration. Firstly, the small sample size may limit the generalizability of our findings. Secondly, the reverberation times used in our experimental setups may not have been long enough to fully capture the impact of room acoustics on cognitive load. Additionally, the study\u0026rsquo;s reliance on pupillometry and self-reports as the sole measures of cognitive load may not encompass all aspects of cognitive effort involved in speech production. While pupillometry provides a valuable objective measure, incorporating other psychophysiological markers could offer a more nuanced understanding of the cognitive processes at play. Finally, the study did not account for individual differences in speech production habits, auditory feedback sensitivity, or previous training in speech modification techniques, all of which could influence how speakers adjust to varying acoustic conditions. Acknowledging these limitations, our findings lay the groundwork for further research aimed at exploring the intricate relationship among cognitive load, speech production, and room acoustics, ultimately guiding the development of more effective communication strategies and therapeutic interventions.\u003c/p\u003e \u003c/div\u003e"},{"header":"CONCLUSION","content":"\u003cp\u003eFindings of this study underscore the importance of understanding the cognitive mechanisms underlying speech production and modification in various acoustic environments. Our findings illuminate the complexities of efforts underlying speech modification, challenging assumptions about the cognitive ease of producing clear speech. Specifically, we demonstrated that clear speech imposes a significant cognitive load on talkers, a load that does not markedly fluctuate with changes in room acoustics. This constancy suggests a potential invariance in cognitive load or a cognitive prioritization that renders acoustic challenges secondary when clarity in speech production is the focus.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgements:\u0026nbsp;\u003c/strong\u003eThis study was supported by the University of Illinois at Urbana-Champaign Campus Research Board Grant RB22019 awarded to the first author (Ishikawa).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability:\u0026nbsp;\u003c/strong\u003eThe data used in this study are not publicly available due to the sensitive nature of the participant information. However, upon request, the corresponding author can provide de-identified data to qualified researchers for the purpose of replication and further analysis.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions:\u0026nbsp;\u003c/strong\u003eK.I. conceived and designed the study. K.I., P.B., S.M., H.L, and E.R. performed the experiments. H.L. and E.R. assisted with data organization. K.I. analyzed the data. K.I. wrote the manuscript and P.B., H.L., and E.R. revised it.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests:\u0026nbsp;\u003c/strong\u003eThe authors declare no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCorresponding author:\u0026nbsp;\u003c/strong\u003eCorrespondence should be addressed to Keiko Ishikawa.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding Statement:\u003c/strong\u003e This study was supported by the University of Illinois at Urbana-Champaign Campus Research Board Research Grant\u0026nbsp;RB22019 awarded to the first author (Ishikawa).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Access Statement:\u003c/strong\u003e The data used in this study are not publicly available due to the sensitive nature of the participant information. Upon request, the corresponding author can provide de-identified data to qualified researchers for the purpose of replication and further analysis.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics declarations:\u0026nbsp;\u003c/strong\u003eThe experimental protocols for this study were approved by the Institutional Review Board of the University of Illinois at Urbana-Champaign (#19215).\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eLindblom, B. in \u003cem\u003eSpeech production and speech modelling\u003c/em\u003e 403\u0026ndash;439 (Springer, 1990).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePeng, Z. E. \u0026amp; Wang, L. M. Listening effort by native and nonnative listeners due to noise, reverberation, and talker foreign accent during English speech perception. 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The Journal of the Acoustical Society of America 151, 4131\u0026ndash;4139 (2022).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSirois, S. \u0026amp; Brisson, J. Pupillometry. Wiley Interdisciplinary Reviews: Cognitive Science 5, 679\u0026ndash;692 (2014).\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":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-4022395/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4022395/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eCommunication is a fundamental aspect of human interaction, yet many individuals must speak in less-than-ideal acoustic environments daily. Adapting their speech to ensure intelligibility in these varied settings can impose a significant cognitive burden. Understanding this burden on talkers has significant implications for the design of public spaces and workplace environments, as well as speaker training programs. The aim of this study was to examine how room acoustics and speaking style affect cognitive load through self-rating of mental demand and pupillometry. Nineteen adult native speakers of American English were instructed to read sentences in both casual and clear speech\u0026mdash;a technique known to enhance intelligibility\u0026mdash;across three levels of reverberation (0.05s, 1.2s, and 1.83s at 500\u0026ndash;1000 Hz). Our findings revealed that speaking style consistently affects the cognitive load on talkers more than room acoustics across the tested reverberation range. Specifically, pupillometry data suggested that speaking in clear speech elevates the cognitive load comparably to speaking in a room with long reverberation, challenging the conventional view of clear speech as an \u0026lsquo;easy\u0026rsquo; strategy for improving intelligibility. These results underscore the importance of accounting for talkers' cognitive load when optimizing room acoustics and developing speech production training.\u003c/p\u003e","manuscriptTitle":"Cognitive Load Associated with Speaking Clearly in Reverberant Rooms","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-03-21 18:43:58","doi":"10.21203/rs.3.rs-4022395/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2024-07-11T05:40:18+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-07-07T06:01:12+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"8850070193934461400062509624199329073","date":"2024-06-24T08:13:01+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-06-23T08:49:43+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"43588261156945840861860475900644159811","date":"2024-06-08T11:13:54+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"b0ffeb84-8ad5-454e-b50c-4437497eb378","date":"2024-03-25T14:00:00+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"7d134efd-4e4a-4d4a-b0e7-b4bb53f17d81","date":"2024-03-25T12:37:21+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2024-03-25T10:40:34+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-03-25T10:28:49+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2024-03-22T09:50:23+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-03-19T12:14:35+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2024-03-06T23:48:17+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"99403879-844e-4f00-b8ca-b82df3e53d18","owner":[],"postedDate":"March 21st, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[{"id":29627148,"name":"Biological sciences/Psychology"},{"id":29627149,"name":"Biological sciences/Psychology/Human behaviour"}],"tags":[],"updatedAt":"2024-09-02T15:59:52+00:00","versionOfRecord":{"articleIdentity":"rs-4022395","link":"https://doi.org/10.1038/s41598-024-70820-w","journal":{"identity":"scientific-reports","isVorOnly":false,"title":"Scientific Reports"},"publishedOn":"2024-08-29 15:57:03","publishedOnDateReadable":"August 29th, 2024"},"versionCreatedAt":"2024-03-21 18:43:58","video":"","vorDoi":"10.1038/s41598-024-70820-w","vorDoiUrl":"https://doi.org/10.1038/s41598-024-70820-w","workflowStages":[]},"version":"v1","identity":"rs-4022395","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4022395","identity":"rs-4022395","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}