Michelle Cohn

I’m a Postdoctoral Scholar in the UC Davis Phonetics Lab. [bio]

I’m also currently a Visiting Researcher with the Google Responsible AI and Human-Centered Technology (HCT) UX Team. (provided by ProUnlimited)

My research program tests the social, cognitive, & emotional factors shaping speech communication in human-human and human-computer interaction. I use a variety of experimental approaches, including methods in psycholinguistics, acoustic-phonetics, and user interaction studies.

I recently completed a 2.5 year National Science Foundation (NSF) Postdoctoral Training Fellowship with with co-PIs Dr. Georgia Zellou (UC Davis Linguistics), Dr. Zhou Yu (Columbia Computer Science), and Dr. Katharine Graf Estes (UC Davis Psychology) to measure the acoustic adjustments adults and kids make when talking to a voice assistant (Alexa) compared to a real human.

My work is interdisciplinary, spanning linguistics, psychology, computer science, and music. I received my Ph.D. in Linguistics in 2018 from UC Davis, working with Dr. Georgia Zellou, Dr. Santiago Barreda, and Dr. Antoine Shahin. I tested the ‘musician’s advantage’ for speech-in-noise perception, testing whether musicians/non-musicians differ in the acoustic cues they use to separate competing talkers.

I recently received the 2021 UC Davis Award for Excellence in Postdoctoral Research, as well as an ‘Honorable Mention’ for the 2021 Chancellor’s Award for Excellence in Mentoring Undergraduate Research.

In 2020, I launched the UC Davis Human-Computer Interaction Research Group: a collective of faculty, postdocs, graduate students, and undergraduates across campus interested in the dynamics of human-computer interaction. Our goal is to form a broader community of scientists, where we can share our work and forge connections across disciplines.

Contact: mdcohn at ucdavis dot edu

Pronouns: she/her/hers

Research Interests

Talking to Tech

How do people talk, perceive, and learn from voice-AI assistants (e.g., Siri, Alexa) compared to real human talkers? …read more!


How do people tailor their speech to improve intelligibility across novel barriers? …read more!


Is individual variation in speech perception shaped by a person’s musical experience? …read more!



  1. Pycha, A., Cohn, M., & Zellou, G. (accepted). Face-masked speech intelligibility: the influence of speaking style, visual information, and background noise. Frontiers in Communication. [Article]
  2. Aoki, N., Cohn, M., & Zellou, G. (accepted). The clear speech intelligibility benefit for text-to-speech voices: Effects of speaking style and visual guise. Journal of Acoustical Society of America (JASA) Express Letters.
  3. Cohn, M., Ferenc Segedin, B., & Zellou, G. (2022). The acoustic-phonetic properties of Siri- and human-DS: Differences by error type and rate. Journal of Phonetics. [Article]
  4. Cohn, M., Predeck, K., Sarian, M., & Zellou, G. (2021). Prosodic alignment toward emotionally expressive speech: Comparing human and Alexa model talkers. Speech Communication. [Article]
  5. Cohn, M., & Zellou, G. (2021). Prosodic differences in human- and Alexa-directed speech, but similar error correction strategies. Frontiers in Communication. [Article]
  6. Cohn, M., Liang, K., Sarian, M., Zellou, G., & Yu, Z. (2021). Speech rate adjustments in conversations with an Amazon Alexa socialbot. Frontiers in Communication [Article]
  7. Zellou, G., Cohn, M., & Kline, T. (2021). The Influence of Conversational Role on Phonetic Alignment toward Voice-AI and Human Interlocutors. Language, Cognition and Neuroscience [Article]
  8. Zellou, G., Cohn, M., Block, A. (2021). Partial compensation for coarticulatory vowel nasalization across concatenative and neural text-to-speech. Journal of the Acoustic Society of America [Article]
  9. Cohn, M., Pycha, A., Zellou, G. (2021). Intelligibility of face-masked speech depends on speaking style: Comparing casual, smiled, and clear speech. Cognition [Article]
  10. Block, A., Cohn, M., & Zellou, G. (2021). Variation in Perceptual Sensitivity and Compensation for Coarticulation Across Adult and Child Naturally-produced and TTS Voices. Interspeech. [Article]
  11. Zellou, G., Cohn, M., Ferenc Segedin, B. (2021). Age- and gender-related differences in speech alignment toward humans and voice-AI. Frontiers in Communication [Article]
  12. Cohn, M. & Zellou, G. (2020). Perception of concatenative vs. Neural text-to-speech (TTS): Differences in intelligibility in noise and language attitudes. Interspeech [pdf] [Virtual Talk]
  13. Cohn, M., Raveh, E., Predeck, K., Gessinger, I., Möbius, B., & Zellou, G. (2020). Differences in Gradient Emotion Perception: Human vs. Alexa Voices. Interspeech [pdf] [Virtual talk]
  14. Zellou, G., & Cohn, M. (2020). Social and functional pressures in vocal alignment: Differences for human and voice-AI interlocutors. Interspeech [pdf]
  15. Cohn, M, Sarian, M., Predeck, K., & Zellou, G. (2020). Individual variation in language attitudes toward voice-AI: The role of listeners’ autistic-like traits. Interspeech [pdf] [Virtual talk]
  16. Cohn, M., Jonell, P., Kim, T., Beskow, J., Zellou, G. (2020). Embodiment and gender interact in alignment to TTS voices. Cognitive Science Society [pdf] [Virtual talk]
  17. Zellou, G., & Cohn, M. (2020). Top-down effects of apparent humanness on vocal alignment toward human and device interlocutors. Cognitive Science Society [pdf]
  18. Zellou, G., Cohn, M., Block, A. (2020). Top-down effect of speaker age guise on perceptual compensation for coarticulatory /u/-fronting. 2020 Cognitive Science Society [pdf]
  19. Yu, D., Cohn, M., Yang, Y.M., Chen, C., … Yu, Z. (2019). Gunrock: A Social Bot for Complex and Engaging Long Conversations. EMNLP-IJCNLP [pdf] Click here for the system demonstration
  20. Cohn, M., Chen, C., & Yu, Z. (2019). A Large-Scale User Study of an Alexa Prize Chatbot: Effect of TTS Dynamism on Perceived Quality of Social Dialog. SIGDial [pdf]
  21. Cohn, M., & Zellou, G. (2019). Expressiveness influences human vocal alignment toward voice-AI. Interspeech [pdf]
  22. Snyder, C. Cohn, M., Zellou, G. (2019). Individual variation in cognitive processing style predicts differences in phonetic imitation of device and human voices. Interspeech [pdf]
  23. Ferenc Segedin, B. Cohn, M., Zellou, G. (2019). Perceptual adaptation to device and human voices:  learning and generalization of a phonetic shift across real and voice-AI talkers. Interspeech [pdf]
  24. Cohn, M., Zellou, G., Barreda, S. (2019). The role of musical experience in the perceptual weighting of acoustic cues for the obstruent coda voicing contrast in American English. Interspeech [pdf]
  25. Cohn, M., Ferenc Segedin, B., Zellou, G. (2019). Imitating Siri: Socially-mediated vocal alignment to device and human voices. ICPhS [pdf]
  26. Brotherton, C., Cohn, M., Zellou, G., Barreda, S. (2019). Sub-regional variation in positioning and degree of nasalization of /æ/ allophones in California. ICPhS [pdf]
  27. Cohn, M. (2018). Investigating a possible “musician advantage” for speech-in-speech perception: The role of f0 separation. Linguistic Society of America [pdf]

Public Outreach

2021 Picnic Day Booth: Speech Science

Come learn about an interdisciplinary research project exploring how adults and kids talk to Amazon’s Alexa, compared to how they talk to a human. You’ll see an example of the experiment, meet the team, and get a behind-the-scenes look at the research process! Interested in participating? http://phonlab.ucdavis.edu/participate