Speech Rate and Interlocutor Identification in Human-Directed VS Device-Directed Arabic Speech

May Nasser Al-Sager, Yahya Aldholmi

Abstract


This study investigates speech rate as both a perceptual cue and a production strategy in Arabic human-directed and device-directed speech. The study comprised two phases. The perception phase examined whether listeners perceive differences in speech rate between human-directed and device-directed speech and whether those differences aid interlocutor identification; 78 native Arabic listeners completed speech-rate rating and speaker-identification tasks. The production phase investigated speech-rate production across interlocutor conditions and sentence types in Najdi Arabic through acoustic analysis of recorded speech. Listeners consistently perceived device-directed speech as faster than human-directed speech and used these temporal differences to identify the intended interlocutor. Production results showed that speakers systematically adjusted speech rate across communicative contexts and sentence types, with shorter sentences eliciting faster rates and speech to a familiar human interlocutor eliciting faster rates than speech to an artificial device. Together, the findings reveal a coupling between speech production and perception, indicating that speech rate functions as a shared communicative cue linking speaker adaptation and listener interpretation. The research contributes to work on speech adaptation, Arabic temporal acoustics, and human–technology interaction by demonstrating that speech rate is not merely a phonetic property of speech but a meaningful temporal signal in communication.

Keywords


Device-Directed Speech; Human-Directed Speech; Speech Perception; Speech Production; Speech Rate

Full Text:

PDF

References


Aldhafeeri, M., Alqahtani, G., & Aldholmi, Y. (2022). Speech rate and intelligibility of Arab Airlines’ safety instructions. In Proceedings of the 13th International Conference of Experimental Linguistics (ExLing 2022) (pp. 5–8). https://doi.org/10.36505/ExLing-2022/13/0002/000544

Aldholmi, Y. (2020). Syllable rate vs. segment rate in perceived speech rate. In Proceedings of the 11th International Conference of Experimental Linguistics. ExLing Society. https://doi.org/10.36505/ExLing-2020/11/0005/000420

Aldholmi, Y. (2022). Medial vowel temporal acoustics in Arabic and Japanese polysyllabic words. Archives of Acoustics, 47(3), 319–329. https://doi.org/10.24425/aoa.2022.142006

Aldholmi, Y. (2024). Statistical metrics for the temporal acoustics of durationally contrastive vocalics: A proposal tested with data from Arabic and Japanese. Archives of Acoustics. https://doi.org/10.24425/aoa.2024.148779

Aldholmi, Y. (2025). AI vs. human (automatic) speech recognition: Silence-replacement paradigm as a diagnostic. Linguistic Proceedings Series, 16th International Conference on Linguistic Research and Applications, Paris, France, 17(2), 5-8. https://doi.org/10.36505/TheLinguisticProceedings/2025/17/02/002/0006

Aldholmi, Y., Aldhafyan, R., & Alqahtani, A. (2021a). Perception of Standard Arabic synthetic speech rate. In Proceedings of Interspeech 2021 (pp. 1704–1707). https://doi.org/10.21437/Interspeech.2021-39

Aldholmi, Y., Alqahtani, A., & Aldhafyan, R. (2021b). Teen production and perception of dialectal Arabic speech rate. In Proceedings of the 12th International Conference of Experimental Linguistics (pp. 13–16). ExLing Society. https://doi.org/10.36505/ExLing-2021/12/0004/000477

Aldholmi, Y., & Park, H. (2016). Perception of speech rate in speech rate perception. The Journal of the Acoustical Society of America, 140(4), 3333. https://doi.org/10.1121/1.4969160

Aldholmi, Y., & Park, H. (2019). Interactions between temporal acoustics and indexical information in speech rate perception. In M. Alhawary (Ed.), Studies in Arabic Linguistics (Vol. 8). John Benjamins. https://doi.org/10.1075/sal.8.10ald

Alghamdi, M. M. (1998). A spectrographic analysis of Arabic vowels: A cross-dialect study. Journal of King Saud University, 10, 3–24. https://chss.ksu.edu.sa/sites/arts.ksu.edu.sa/files/imce_images/v10m190r1962.pdf

Alshaikhmubarak, L., & Aldholmi, Y. (2025). The influence of native language rhythm on speech rate perception in unfamiliar languages. Journal of Arabic Sciences & Humanities, 18(4), 9–49. https://doi.org/10.71422/0198-018-004-001

Alshaikhmubarak, L., & Aldholmi, Y. (2026). The effect of rhythmic system on the perception of L1, L2 and unfamiliar languages. Ijaz Arabi: Journal of Arabic Learning, 9(1), 64–82. https://doi.org/10.18860/ijazarabi.V9i1.32966

Bell, A. (1984). Language style as audience design. Language in Society, 13(2), 145–204. https://doi.org/10.1017/S004740450001037X

Bradlow, A. R., & Bent, T. (2002). The clear speech effect for non-native listeners. The Journal of the Acoustical Society of America, 112(1), 272–284. https://doi.org/10.1121/1.1487837

Buz, E., Tanenhaus, M. K., & Jaeger, T. F. (2016). Dynamically adapted context-specific hyper-articulation: Feedback from interlocutors affects speakers’ subsequent pronunciations. Journal of Memory and Language, 89, 68–86. https://doi.org/10.1016/j.jml.2015.12.009

Cohn, M., Barreda, S., Graf Estes, K., Yu, Z., & Zellou, G. (2024a). Children and adults produce distinct technology- and human-directed speech. Scientific Reports, 14, 15611. https://doi.org/10.1038/s41598-024-66313-5

Cohn, M., Liang, K.-H., Sarian, M., Zellou, G., & Yu, Z. (2021a). Speech rate adjustments in conversations with an Amazon Alexa socialbot. Frontiers in Communication, 6, 671429. https://doi.org/10.3389/fcomm.2021.671429

Cohn, M., Mengesha, Z., Lahav, M., & Heldreth, C. (2024b). African American English speakers’ pitch variation and rate adjustments for imagined technological and human addressees. JASA Express Letters, 4(4). https://doi.org/10.1121/10.0025484

Cohn, M., Segedin, B. F., & Zellou, G. (2022). Acoustic-phonetic properties of Siri- and human-directed speech. Journal of Phonetics, 90, 101123. https://doi.org/10.1016/j.wocn.2021.101123

Cohn, M., & Zellou, G. (2021b). Prosodic differences in human- and Alexa-directed speech, but similar local intelligibility adjustments. Frontiers in Communication, 6, 675704. https://doi.org/10.3389/fcomm.2021.675704

Cohn, M., & Zellou, G. (2021). Prosodic Differences in Human- And Alexa-Directed Speech, but Similar Local Intelligibility Adjustments. Frontiers in Communication, 6(July), 1–13. https://doi.org/10.3389/fcomm.2021.675704

Ferrat, K., & Guerti, M. (2011). Acoustic and articulatory analysis of the gemination in Modern Standard Arabic. Al-Lisaniyyat, 21, 73–88. https://doi.org/10.61850/allj.v21i2.306

Field, A. (2013). Discovering statistics using IBM SPSS statistics (4th ed.). SAGE Publications.

Gessinger, I., Raveh, E., Steiner, I., & Möbius, B. (2021). Phonetic accommodation to natural and synthetic voices: Behavior of groups and individuals in speech shadowing. Speech Communication, 127, 43–63. https://doi.org/10.1016/j.specom.2020.12.004

Giles, H., & Coupland, N. (1991). Language: Contexts and consequences. Open University Press.

Giroud, J., Lei, J., Phillips, K., & Davis, M. (2024). Behavioral evidence for higher speech rate convergence following natural than artificial time altered speech. Proceedings of Interspeech 2024. https://doi.org/10.21437/Interspeech.2024-1001

Hay, J., & Drager, K. (2010). Stuffed toys and speech perception. Linguistics, 48(4), 865–892. https://doi.org/10.1515/ling.2010.027

Jacewicz, E., Fox, R. A., & Wei, L. (2010). Between-speaker and within-speaker variation in speech tempo of American English. The Journal of the Acoustical Society of America, 128(2), 839–850. https://doi.org/10.1121/1.3459842

Lindblom, B. (1990). Explaining phonetic variation: A sketch of the H&H theory. In W. J. Hardcastle & A. Marchal (Eds.), Speech production and speech modelling (NATO ASI Series, Vol. 55, pp. 403–439). Springer. https://doi.org/10.1007/978-94-009-2037-8_16

McGettigan, C. (2015). The social life of voices: Studying the neural bases for the expression and perception of the self and others during spoken communication. Frontiers in Human Neuroscience, 9, 129. https://doi.org/10.3389/fnhum.2015.00129

Mitleb, F. (1984). Vowel length contrast in Arabic and English: A spectrographic test. Journal of Phonetics, 12(3), 229–235. https://doi.org/10.1016/S0095-4470(19)30879-4

Niedzielski, N. (1999). The effect of social information on the perception of sociolinguistic variables. Journal of Language and Social Psychology, 18(1), 62–85. https://doi.org/10.1177/0261927X99018001005

Picheny, M. A., Durlach, N. I., & Braida, L. D. (1986). Speaking clearly for the hard of hearing I: Intelligibility differences between clear and conversational speech. Journal of Speech and Hearing Research, 29, 434–446. https://doi.org/10.1044/jshr.2904.434

Siegert, I., & Krüger, J. (2021). Speech melody and speech content didn’t fit together: Differences in speech behavior for device-directed and human-directed interactions. In Advances in Data Science (pp. 65–95). https://doi.org/10.1007/978-3-030-51870-7_4

Smiljanıć, R., & Bradlow, A. R. (2005). Production and perception of clear speech in Croatian and English. The Journal of the Acoustical Society of America, 118(3), 1677–1688. https://doi.org/10.1121/1.2000788

Steffman, J. (2021). Rhythmic and speech rate effects in the perception of durational cues. Attention, Perception, and Psychophysics, 83(8), 3162–3182. https://doi.org/10.3758/s13414-021-02334-w

Vonessen, J., Aoki, N. B., Cohn, M., & Zellou, G. (2024). Comparing perception of L1 and L2 English by human listeners and machines: Effect of interlocutor adaptations. The Journal of the Acoustical Society of America, 155(5), 3060–3070. https://doi.org/10.1121/10.0025930

Watson, J. C. E. (2007). The phonology and morphology of Arabic. Oxford University Press.

Zellou, G., & Cohn, M. (2023a). Clear speech in the new digital era: Speaking and listening clearly to voice-AI systems. The Journal of the Acoustical Society of America, 153(3). https://doi.org/10.1121/10.0018378

Zellou, G., Cohn, M., & Pycha, A. (2023b). Listener beliefs and perceptual learning: Differences between device and human guises. Language, 99(4), 692–725. https://dx.doi.org/10.1353/lan.2023.a914191

Zellou, G., & Holliday, N. (2024). Linguistic analysis of human-computer interaction. Frontiers in Computer Science, 6, 1384252. https://doi.org/10.3389/fcomp.2024.1384252




DOI: https://doi.org/10.18860/ijazarabi.v9i4.44864

Refbacks

  • There are currently no refbacks.


Copyright (c) 2026 May Nasser Al-Sager, Yahya Aldholmi

License URL: https://creativecommons.org/licenses/by-sa/4.0/