Elizabeth Shriberg

Pauses in Deceptive Speech

We use a corpus of spontaneous interview speech to investigate the relationship between the distributional and prosodic characteristics of silent and filled pauses and the intent of an interviewee to deceive an interviewer. Our data suggest that the use of pauses correlates more with truthful than with deceptive speech, and that prosodic features extracted from filled pauses themselves as well as features describing contextual prosodic information in the vicinity of filled pauses may facilitate the detection of deceit in speech.

Personality Factors in Human Deception Detection: Comparing Human to Machine Performance

Previous studies of human performance in deception detection have found that humans generally are quite poor at this task, comparing unfavorably even to the performance of automated procedures. However, different scenarios and speakers may be harder or easier to judge. In this paper we compare human to machine performance detecting deception on a single corpus, the ColumbiaSRI-Colorado Corpus of deceptive speech. On average, our human judges scored worse than chance — and worse than current best machine learning performance on this corpus. However, not all judges scored poorly. Based on personality tests given before the task, we find that several personality factors appear to correlate with the ability of a judge to detect deception in speech. Index Terms: deception, deceptive, perception, personality.

Detecting Deception Using Critical Segments

We present an investigation of segments that map to GLOBAL LIES, that is, the intent to deceive with respect to salient topics of the discourse. We propose that identifying the truth or falsity of these CRITICAL SEGMENTS may be important in determining a speaker’s veracity over the larger topic of discourse. Further, answers to key questions, which can be identified ap riori, may represent emotional and cognitive HOT SPOTS, analogous to those observed by psychologists who study gestural and facial cues to deception. We present results of experiments that use two different definitions of CRITICAL SEGMENTS and employ machine learning techniques that compensate for imbalances in the dataset. Using this approach, we achieve a performance gain of 23.8% relative to chance, in contrast with human performance on a similar task, which averages substantially below chance. We discuss the features used by the models, and consider how these findings can influence future research. Index Terms: deception, deceptive, speech

Corpus-independent history compression for stochastic turn-taking models

Stochastic turn-taking models use a truncated representation of past speech activity to specify how likely a speaker is to talk at the next instant. An unanswered question in such modeling is how far back to extend the conditioning context. We study this question using Switchboard (English, telephone) and Spontal (Swedish, face-to-face) conversations. We also explore whether to trade off precision with range when moving backward in the history. We find that (1) a nearly logarithmic compression of history is optimal, for both speaker and interlocutor; (2) the absolute duration of the conditioning context is at least 7 seconds; and (3) the compression scheme generalizes remarkably well across the two different corpora.

Speaker recognition with region-constrained MLLR transforms

It has been shown that standard cepstral speaker recognition models can be enhanced by region-constrained models, where features are extracted only from certain speech regions defined by linguistic or prosodic criteria. Such region-constrained models can capture features that are more stable, highly idiosyncratic, or simply complementary to the baseline system. In this paper we ask if another major class of speaker recognition models, those based on MLLR speaker adaptation transforms, can also benefit from region-constrained feature extraction. In our approach, we define regions based on phonetic and prosodic criteria, based on automatic speech recognition output, and perform MLLR estimation using only frames selected by these criteria. The resulting transform features are appended to those of a state-of-the-art MLLR speaker recognition system and jointly modeled by SVMs. Multiple regions can be added in this fashion. We find consistent gains over the baseline system in the SRE2010 speaker verification task.