Prof. Emily Mower Provost
Emily Mower Provost is a Professor and Associate Chair for Graduate Affairs in Computer Science and Engineering 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 on the paper, “Say Cheese vs. Smile: Reducing Speech-Related Variability for Facial Emotion Recognition,” winner of Best Student Paper at ACM Multimedia, 2014, and a co-author of the winner of the Classifier Sub-Challenge event at the Interspeech 2009 emotion challenge. 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.
Emotion has intrigued researchers for generations. This fascination has permeated the engineering community, motivating the development of affective computational models for classification. However, human emotion remains notoriously difficult to interpret both because of the mismatch between the emotional cue generation (the speaker) and cue perception (the observer) processes and because of the presence of complex emotions, emotions that contain shades of multiple affective classes. Proper representations of emotion would ameliorate this problem by introducing multidimensional characterizations of the data that permit the quantification and description of the varied affective components of each utterance. Currently, the mathematical representation of emotion is an area that is underexplored. Research in emotion expression and perception provides a complex and human-centered platform for the integration of machine learning techniques and multimodal signal processing towards the design of interpretable data representations.
Behavioral modeling has important application in the field of assistive technology. In this sphere, it becomes critical to understand how a clinician will perceive the behavior of a patient. Our work focuses on methods to recognize mood for individuals with bipolar disorder and methods to estimate speech intelligibility for people with aphasia.