Detecting Deceptive Speech
Julia Hirschberg, Columbia University
October 2, 2007
This talk will discuss production and perception studies of deceptive speech and the acoustic/prosodic and lexical cues associated with deception. Experiments in which we collected a large corpus of deceptive and non-deceptive speech from naive subjects in the laboratory are described, together with perception experiments of this corpus. Features extracted from this corpus have been used in Machine Learning experiments to predict deception with classification accuracy from 64.0- 66.4%, depending upon feature-set and learning algorithm. This performance compares favorably with the performance of human judges on the same data and task, which averaged 58.2%. We also discuss current findings on the role of personality factors in deception detection, speaker-dependent models of deception, and future research. This work was done in collaboration with Frank Enos, Columbia University;Elizabeth Shriberg, Andreas Stolcke, and Martin Graciarena, SRI/ICSI; Stefan Benus, Brown University; and more.
Julia Hirschberg is Professor of Computer Science at Columbia University. From 1985-2003 she worked at Bell Labs and AT&T Labs, as member of Technical Staff working on intonation assignment in text-to-speech synthesis and then as Head of the Human Computer Interaction Research Department. Her research focusses on prosody in speech generation and understanding. She currently works on speech summarization, emotional speech, charismatic speech, deceptive speech, and dialogue prosody. Hirschberg was President of the International Speech Communication Association from 2005-2007 and co-editor-in-chief of Speech Communication from 2003-2006. She was editor-in-chief of Computational Linguistics and on the board of the Association for Computational Linguistics from 1993-2003. She has been a fellow of the American Association for Artificial Intelligence since 1994.