Michelle Cohn

dblp:250/2898 · DBLP profile ↗
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18ranked-venue papers
9as first author
6since 2021 · last 2026
0000-0002-4847-1464ORCID · verified

Domains — the database's venue-derived domains; a paper can count in several

Artificial intelligence and machine learning · 15 · 7 first-author · 3 since 2021Graphics, computer vision, multimedia, augmented reality and games · 13 · 6 first-author · 5 since 2021Applied, interdisciplinary, general and emerging computing · 3 · 1 first-authorHuman-computer interaction and ubiquitous computing · 1 · 1 first-author · 1 since 2021
YearPublicationVenuePosition
2026 Challenges in Automatic Speech Recognition for Adults with Cognitive Impairment
Michelle Cohn, Alyssa Lanzi, Yui Ishihara, Chen-Nee Chuah, Georgia Zellou, Alyssa Weakley
CHI1
2025 Social evaluation of text-to-speech voices by adults and children
Kevin D. Lilley, Ellen Dossey, Michelle Cohn, Cynthia G. Clopper, Georgia Zellou
Speech Commun.3
2023 Cross-linguistic Emotion Perception in Human and TTS Voices
Iona Gessinger, Michelle Cohn, Benjamin R. Cowan, Georgia Zellou, Bernd Möbius
INTERSPEECH2
2022 Cross-Cultural Comparison of Gradient Emotion Perception: Human vs. Alexa TTS Voices
Iona Gessinger, Michelle Cohn, Georgia Zellou, Bernd Möbius
INTERSPEECH2
2021 Variation in Perceptual Sensitivity and Compensation for Coarticulation Across Adult and Child Naturally-Produced and TTS Voices
Aleese Block, Michelle Cohn, Georgia Zellou
Interspeech2
2021 Prosodic alignment toward emotionally expressive speech: Comparing human and Alexa model talkers
abstract
This study tests whether individuals vocally align toward emotionally expressive prosody produced by two types of interlocutors: a human and a voice-activated artificially intelligent (voice-AI) assistant. Participants completed a word shadowing experiment of interjections (e.g., “Awesome”) produced in emotionally neutral and expressive prosodies by both a human voice and a voice generated by a voice-AI system (Amazon's Alexa). Results show increases in participants’ word duration, mean f0, and f0 variation in response to emotional expressiveness, consistent with increased alignment toward a general ‘positive-emotional’ speech style. Small differences in emotional alignment by talker category (human vs. voice-AI) parallel the acoustic differences in the model talkers’ productions, suggesting that participants are mirroring the acoustics they hear. The similar responses to emotion in both a human and voice-AI talker support accounts of unmediated emotional alignment, as well as computer personification: people apply emotionally-mediated behaviors to both types of interlocutors. While there were small differences in magnitude by participant gender, the overall patterns were similar for women and men, supporting a nuanced picture of emotional vocal alignment.
Michelle Cohn, Kristin Predeck, Melina Sarian, Georgia Zellou
Speech Commun.1
2020 Embodiment and gender interact in alignment to TTS voices
Michelle Cohn, Patrik Jonell, Taylor Kim, Jonas Beskow, Georgia Zellou
CogSci1
2020 Top-down effects of apparent humanness on vocal alignment toward human and device interlocutors
Georgia Zellou, Michelle Cohn
CogSci2
2020 Does top-down information about speaker age guise influence perceptual compensation for coarticulatory /u/-fronting?
Georgia Zellou, Michelle Cohn, Aleese Block
CogSci2
2020 Differences in Gradient Emotion Perception: Human vs. Alexa Voices
Michelle Cohn, Eran Raveh, Kristin Predeck, Iona Gessinger, Bernd Möbius, Georgia Zellou
INTERSPEECH1
2020 Individual Variation in Language Attitudes Toward Voice-AI: The Role of Listeners' Autistic-Like Traits
Michelle Cohn, Melina Sarian, Kristin Predeck, Georgia Zellou
INTERSPEECH1
2020 Perception of Concatenative vs. Neural Text-To-Speech (TTS): Differences in Intelligibility in Noise and Language Attitudes
Michelle Cohn, Georgia Zellou
INTERSPEECH1
2020 Social and Functional Pressures in Vocal Alignment: Differences for Human and Voice-AI Interlocutors
Georgia Zellou, Michelle Cohn
INTERSPEECH2
2019 Expressiveness Influences Human Vocal Alignment Toward voice-AI
Michelle Cohn, Georgia Zellou
INTERSPEECH1
2019 The Role of Musical Experience in the Perceptual Weighting of Acoustic Cues for the Obstruent Coda Voicing Contrast in American English
Michelle Cohn, Georgia Zellou, Santiago Barreda
INTERSPEECH1
2019 Perceptual Adaptation to Device and Human Voices: Learning and Generalization of a Phonetic Shift Across Real and Voice-AI Talkers
Bruno Ferenc Segedin, Michelle Cohn, Georgia Zellou
INTERSPEECH2
2019 Individual Variation in Cognitive Processing Style Predicts Differences in Phonetic Imitation of Device and Human Voices
Cathryn Snyder, Michelle Cohn, Georgia Zellou
INTERSPEECH2
2019 A Large-Scale User Study of an Alexa Prize Chatbot: Effect of TTS Dynamism on Perceived Quality of Social Dialog
abstract
This study tests the effect of cognitiveemotional expression in an Alexa text-tospeech (TTS) voice on users' experience with a social dialog system.We systematically introduced emotionally expressive interjections (e.g., "Wow!") and filler words (e.g., "um", "mhmm") in an Amazon Alexa Prize socialbot, Gunrock.We tested whether these TTS manipulations improved users' ratings of their conversation across thousands of real user interactions (n=5,527).Results showed that interjections and fillers each improved users' holistic ratings, an improvement that further increased if the system used both manipulations.A separate perception experiment corroborated the findings from the user study, with improved social ratings for conversations including interjections; however, no positive effect was observed for fillers, suggesting that the role of the rater in the conversation-as active participant or external listener-is an important factor in assessing social dialogs.
Michelle Cohn, Zhou Yu 0005
SIGdial1