Speaker: Sarah Ita Levitan, Columbia University
Title: True or False? Automatic Detection of Deception and Trust in Spoken Dialogue
Spoken language processing (SLP) aims to teach computers to understand human speech. Automatic deception detection from speech is one of the few problems in AI where machines can potentially perform significantly better than humans, who can only detect lies about 50% of the time. In this talk, I will discuss my work on training computers to distinguish between deceptive and truthful speech using language features. My work combines machine learning with insights from psychology and linguistics to develop robust techniques to detect deceptive speech. I will also present ongoing research aimed at understanding the characteristics of trustworthy language. This work improves our scientific understanding of deception and trust, and has implications for security applications and for increasing trust in human-computer interaction.
Sarah Ita Levitan is a postdoctoral Research Scientist in the Department of Computer Science at Columbia University. Her research interests are in spoken language processing, and she is currently working on identifying acoustic-prosodic and linguistic indicators of trustworthy speech, as well as identifying linguistic characteristics of trustworthy news. She received her PhD in Computer Science at Columbia University, advised by Dr. Julia Hirschberg, and her dissertation addressed the problem of automatic deception detection from speech. Sarah Ita was a 2018 Knight News Innovation Fellow and a recipient of the NSF Graduate Research Fellowship and the NSF IGERT From Data to Solutions fellowship. She has interned at Google Research and at Interactions LLC.