Detecting deception in speech.

摘要

This dissertation describes work on the detection of deception in speech using the techniques of spoken language processing. The accurate detection of deception in human interactions has long been of interest across a broad array of contexts and has been studied in a number of fields, including psychology, communication, and law enforcement. The detection of deception is well-known to be a challenging problem: people are notoriously bad lie detectors, and no verified approach yet exists that can reliably and consistently catch liars.;To date, the speech signal itself has been largely neglected by researchers as a source of cues to deception. Prior to the work presented here, no comprehensive attempt has been made by speech scientists to apply state-of-the-art speech processing techniques to the study of deception. This work uses a set of features new to the deception domain in classification experiments, statistical analyses, and speaker- and group-dependent modeling approaches, all designed to identify and employ potential cues to deception in speech.;This dissertation shows that speech processing techniques are relevant to the deception domain by demonstrating significant statistical effects for deception on a number of features, both in corpus-wide and subject-dependent analyses. Results also show that deceptive speech can be automatically classified with some success: accuracy is better than chance and considerably better than human hearers performing an analogous task. The work also examines speaker and group differences with respect to deceptive speech, and we report a number of findings in this regard. We provide a context for our work via a perception study in which human hearers attempted to identify deception in our corpus. Through this perception study we identify a number of previously unreported effects that relate the personality of the hearer to deception detection ability. An additional product of this work is the CSC Corpus, a new corpus of deceptive speech.