Acoustic-prosodic indicators of deception and trust in interview dialogues

We analyze a set of acoustic-prosodic features in both truthful and deceptive responses to interview questions, identifying differences between truthful and deceptive speech. We also study the perception of deception, identifying acoustic-prosodic characteristics of speech that is perceived as truthful or deceptive by interviewers. In addition to studying differences across all speakers, we identify variations in deception production and perception across gender and native language. We conduct machine learning classification experiments aimed at distinguishing between truthful and deceptive speech, using acoustic-prosodic features. We also explore methods of leveraging individual traits for deception classification. Our results show that acoustic-prosodic features are highly effective at classifying deceptive speech. Our best classifier achieved an F1-score of 72.77, well above both the random baseline and above human performance at this task. This work advances our understanding of deception production and perception, and has implications for automatic deception detection and the development of synthesized speech that is trustworthy.