Automatic detection of deceit in verbal communication

Mihalcea, Rada; Pérez-Rosas, Verónica; Burzo, Mihai

doi:10.1145/2522848.2522888

Cited by 11 publications

(11 citation statements)

References 17 publications

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“…The features display the frequencies of occurrences of classes, derived from the occurrences of words, attributed to each class. The lexicon has been successfully used in previous work for automatic deception detection (Ott et al, 2013;Mihalcea et al, 2013). Part of Speech tags (PoS).…”

Section: Linguistic Featuresmentioning

confidence: 99%

Box of Lies: Multimodal Deception Detection in Dialogues

Soldner¹,

Pérez-Rosas²,

Mihalcea³

2019

Proceedings of the 2019 Conference of the North

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Deception often takes place during everyday conversations, yet conversational dialogues remain largely unexplored by current work on automatic deception detection. In this paper, we address the task of detecting multimodal deceptive cues during conversational dialogues. We introduce a multimodal dataset containing deceptive conversations between participants playing The Tonight Show Starring Jimmy Fallon R Box of Lies game, in which they try to guess whether an object description provided by their opponent is deceptive or not. We conduct annotations of multimodal communication behaviors, including facial and linguistic behaviors, and derive several learning features based on these annotations. Initial classification experiments show promising results, performing well above both a random and a human baseline, and reaching up to 69% accuracy in distinguishing deceptive and truthful behaviors.

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Section: Linguistic Featuresmentioning

confidence: 99%

Box of Lies: Multimodal Deception Detection in Dialogues

Soldner¹,

Pérez-Rosas²,

Mihalcea³

2019

Proceedings of the 2019 Conference of the North

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“…We list here results for a few other cases, which can also be used to check the general formulas above. For p = 1 [95], [96][prop. 3.5.5], c 2 (S * ) = σ , ch 2 (S * ) = (1/2)σ 2 − σ , and in fact c i (S * ) is given by the Schubert cycle associated to the Young diagram with i vertical boxes.…”

Section: Representations Of U (K)mentioning

confidence: 99%

Notes on nonabelian (0,2) theories and dualities

Jia

Sharpe

2014

J. High Energ. Phys.

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In this paper we explore basic aspects of nonabelian (0,2) GLSMs in two dimensions for unitary gauge groups, an arena that until recently has largely been unexplored. We begin by discussing general aspects of (0,2) theories, including checks of dynamical supersymmetry breaking, spectators and weak coupling limits, and also build some toy models of (0,2) theories for bundles on Grassmannians, which gives us an opportunity to relate physical anomalies and trace conditions to mathematical properties. We apply these ideas to study (0,2) theories on Pfaffians, applying recent perturbative constructions of Pfaffians of Jockers et al. . We discuss how existing dualities in (2,2) nonabelian gauge theories have a simple mathematical understanding, and make predictions for additional dualities in (2,2) and (0,2) gauge theories. Finally, we outline how duality works in open strings in unitary gauge theories, and also describe why, in general terms, we expect analogous dualities in (0,2) theories to be comparatively rare.

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“…Corpora of deceptive and non-deceptive speech have been built, such as the Columbia/SRI/Colorado (CSC) corpus [18]. Four kinds of cues were used: three computed from the speech signal (prosodic [19], [18], and [20], spectral [21] and nonlinear features [22]), and the fourth coming from the analysis of the lexical content [21], [23], and [24].…”

Section: Introductionmentioning

confidence: 99%

Prosodic Cues and Answer Type Detection for the Deception Sub-Challenge

Montacié

Caraty

2016

Interspeech 2016

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Deception is a deliberate act to deceive interlocutor by transmitting a message containing false or misleading information. Detection of deception consists in the search for reliable differences between liars and truth-tellers. In this paper, we used the Deceptive Speech Database (DSD) provided for the Deception sub-challenge. DSD consists of deceptive and non-deceptive answers to a set of unknown questions. We have investigated linguistic cues: prosodic cues (pauses and phone duration, speech segmentation) and answer types (e.g., opinion, self-report, offense denial). These cues were automatically detected using the CMU-Sphinx toolkit for speech recognition (acoustic-phonetic decoding, isolated word recognition and keyword spotting). Two kinds of prosodic features were computed from the speech transcriptions (phoneme, silent pause, filled pause, and breathing): the usual speech rate measures and the audio feature based on the multiresolution paradigm. The answer type features were introduced. A set of answer types was chosen from the transcription of the Training set and each answer type was modeled by a bag-of-words. Experiments have shown improvements of 13.0% and 3.8% on the Development and Test sets respectively, compared to the official baseline Unweighted Average Recall.

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Automatic detection of deceit in verbal communication

Cited by 11 publications

References 17 publications

Box of Lies: Multimodal Deception Detection in Dialogues

Box of Lies: Multimodal Deception Detection in Dialogues

Notes on nonabelian (0,2) theories and dualities

Prosodic Cues and Answer Type Detection for the Deception Sub-Challenge

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