Emily Mower Provost

Assistant Professor, Computer Science and Engineering

Emily Mower Provost

Assistant Professor, Computer Science and Engineering

My Portrait

Emily Mower Provost

Assistant Professor

University of Michigan
EECS Department
Computer Science & Engineering
3620 BBB
Ann Arbor, MI 48109-2121
Tel: 734-647-1802
Email:



My Portrait

Computational Human-Centered Analysis and Integration

Bio

Emily Mower Provost is an Assistant Professor in Computer Science and Engineering at the University of Michigan. She received her B.S. in Electrical Engineering (summa cum laude and with thesis honors) from Tufts University, Boston, MA in 2004 and her M.S. and Ph.D. in Electrical Engineering from the University of Southern California (USC), Los Angeles, CA in 2007 and 2010, respectively. She is a member of Tau-Beta-Pi, Eta-Kappa-Nu, and a member of IEEE and ISCA. She has been awarded the National Science Foundation Graduate Research Fellowship (2004-2007), the Herbert Kunzel Engineering Fellowship from USC (2007-2008, 2010-2011), the Intel Research Fellowship (2008-2010), the Achievement Rewards For College Scientists (ARCS) Award (2009 – 2010), and the Oscar Stern Award for Depression Research (2015). 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. She is also a co-author of the winner paper 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 human emotion generation and perception.

Research Overview

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.



Recorded Talks