Joint Learning of Conversational Temporal Dynamics and Acoustic Features for Speech Deception Detection in Dialog Games

Chou, Huang-Cheng; Liu, Yiwen; Lee, Chi-Chun

doi:10.1109/apsipaasc47483.2019.9023050

Cited by 9 publications

(17 citation statements)

References 30 publications

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“…The further analyses reveal several insights. Firstly, we observe the same findings in acoustic and conversational temporal dynamics feature set as [20]. Secondly, the truthtellers have a higher proportion of complications than the liars, which is the same observation as the previous study [22] that was conducted on the English native speakers.…”

supporting

confidence: 84%

“…In this work, we follow the same setting as the previous work [20] that pool utterances into QA pair turns. The interrogator tends to ask a series of questions to elicit answers from the deceiver to help recognize the deceptive/truthful statements for each topic.…”

Section: I T H E D D D M D a T A B A S Ementioning

confidence: 99%

“…More specifically, pragmatic behaviors contain the deceivers' non-verbal, verbal cues, and abnormal behaviors, and implicature behaviors describe deceivers' metaphors. Besides, there are two phases in the framework: in-phase (i): we model deceivers' [20], respectively. Further, BERT P means the word-level textual embeddings extracted by BERT pretrained model, and it also includes non-verbal and pragmatic behavior information.…”

Section: I I R E S E a R C H M E T H O D O L O G Ymentioning

confidence: 99%

“…those in psychology, social science, and conversational linguistics, have already shown many of these aspects are related to the expression of deceptions, very few computational works, if any, have explicitly considered these to improve the automatic deception model. Only recently, Chou et al [20] showed that conversation temporal dynamics can be integrated as input features to help improve deception detection, where many of their derived features are inspired by studies of conversation scholars [21]. In this work, our goal is to integrate comprehensive speech and language communicative descriptors into constructing an automatic deception detection model.…”

mentioning

confidence: 99%

“…The previous work [20] was the first in modeling the conversational temporal dynamics based on dyadic turn-taking behaviors. To be more specific, they designed 20-dimensional temporal features in each "questioninganswering" (QA) pair composed of a questioning turn and an answering turn.…”

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confidence: 99%

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Automatic Deception Detection using Multiple Speech and Language Communicative Descriptors in Dialogs

Chou

Liu

Lee

2021

SIP

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While deceptive behaviors are a natural part of human life, it is well known that human is generally bad at detecting deception. In this study, we present an automatic deception detection framework by comprehensively integrating prior domain knowledge in deceptive behavior understanding. Specifically, we compute acoustics, textual information, implicatures with non-verbal behaviors, and conversational temporal dynamics for improving automatic deception detection in dialogs. The proposed model reaches start-of-the-art performance on the Daily Deceptive Dialogues corpus of Mandarin (DDDM) database, 80.61% unweighted accuracy recall in deception recognition. In the further analyses, we reveal that (i) the deceivers’ deception behaviors can be observed from the interrogators’ behaviors in the conversational temporal dynamics features and (ii) some of the acoustic features (e.g. loudness and MFCC) and textual features are significant and effective indicators to detect deception behaviors.

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supporting

confidence: 84%

Section: I T H E D D D M D a T A B A S Ementioning

confidence: 99%

Section: I I R E S E a R C H M E T H O D O L O G Ymentioning

confidence: 99%

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confidence: 99%

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