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Deceptive Speech Julia Hirschberg, Stefan Benus, and I, along with colleagues from SRI/ICSI (and formerly the University of Colorado at Boulder), are working on distinguishing deceptive from non-deceptive speech. Our ongoing work focuses on detection of deception by applying machine learning techniques to acoustic, prosodic, and linguistic features extracted from the Columbia-SRI-Colorado Deception Corpus. This corpus was collected at Columbia and represents a paradigm in which subjects lied to an interviewer with respect to their performance on a series of tests. The interviewer interrogates the subject with respect to their performance in the six test areas; subjects are directed to claim that their scores matched an ideal profile. Preliminary results using machine learning experiments are reported in our Eurospeech 2005 paper. In addition, we have conducted a perception study that assesses human judges' ability to distinguish between deceptive and truthful speech in this corpus. Our intention here is both to establish a human baseline for our classification task, and to identify and examine subjects that are particularly easy or particularly difficult for humans to classify. We have also been working with Robin Cautin to discover attributes, such as personality factors, that make humans better (or worse) detectors of deception. Our results do, in fact, indicate that such factors exist. Our other work on this corpus includes an examination of the use of filled pauses as a cue to deceptive speech, and the combination of multiple learners using different feature sets to create a single classifier.
Detecting Deception in Speech
Detecting Deception Using Critical Segments
Personality factors in human deception detection:
Pauses in Deceptive Speech
Combining Prosodic, Lexical and Cepstral Systems for Deceptive Speech Detection
Distinguishing Deceptive from Non-Deceptive Speech
Related Work I am also involved in a number of outside projects on deception. In the summer of 2005, I participated in the NSF's Workshop on Behavioral Aspects of Security, organized by Mark Frank of the University at Buffalo. In 2004, I participated in the Workshop on Detecting Deception in Language and Cultural Context at U.M.D.'s Center for the Advanced Study of Language (CASL). I have worked with CASL on a number of projects relating to deception in various cultures and languages. Emotional Speech My ongoing interests include the development of a model of emotion for use in this area, and I am particularly intrigued in the intersection of speaker intention with context, and how that intersection may generate what is perceived in speech (and otherwise) as emotion. At the LREC 2006 Workshop on Corpora for Research on Emotion and Affect I presented a paper written with Julia Hirschberg on a novel approach to eliciting emotional speech from actors using the methods employed in theater rehearsal. Unlike approaches that ask the actor to pretend to have the relevant emotion, this approach induces emotion by taking advantage of techniques developed in the professional theater for this purpose. This approach has the advantage of allowing the induction of a tremendous range of emotions, both positive and negative, in an ethical manner, since professional actors are, by virtue of their training and experience, accustomed to dealing with the induction of negative emotions.
A framework for eliciting emotional speech:
Computational Linguistics and the Performing Arts |
Frank Enos 726 CEPSR 212.939.7122 frank [æt] cs.columbia.edu |