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Emotions provide critical cues into our health and wellbeing. This is of particular importance in the context of mental health, where changes in emotion may signify changes in symptom severity. However, information about emotion and how it varies over time is often accessible only using survey methodology (e.g., ecological momentary assessment, EMA), which can become burdensome to participants over time. Automated speech emotion recognition systems could provide an alternative, providing quantitative measures of emotion using acoustic data captured passively from a consented individual’s environment. However, speech emotion recognition systems often falter when presented with data collected from unconstrained natural environments due to issues with robustness, generalizability, and invalid assumptions. In this talk, I will discuss our journey in speech-centric mental health modeling, explaining whether, how, and when emotion recognition can be applied to natural unconstrained speech data to measure changes in mental health symptom severity. Bio
Emily Mower Provost is a Professor and Senior Associate Chair in Computer Science and Engineering and Professor of Psychiatry (by courtesy) at the University of Michigan. She received her Ph.D. in Electrical Engineering from the University of Southern California (USC), Los Angeles, CA in 2010. She has been awarded a Toyota Faculty Scholar Award (2020), National Science Foundation CAREER Award (2017), the Oscar Stern Award for Depression Research (2015), a National Science Foundation Graduate Research Fellowship (2004-2007). She is a co-author of multiple award-winning papers in the field of automatic emotion recognition. Her research interests are in human-centered speech and video processing, multimodal interfaces design, and speech-based assistive technology. The goals of her research are motivated by the complexities of the perception and expression of human behavior. This talk will NOT be streamed live and recorded. |
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