Detecting Deceptive Speech

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.