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

IF 3.2 Q1 Computer Science
Huang-Cheng Chou, Yi-Wen Liu, Chi-Chun Lee
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引用次数: 5

Abstract

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.
对话中使用多个语音和语言交际描述符的自动欺骗检测
虽然欺骗行为是人类生活中很自然的一部分,但众所周知,人类通常不善于识破欺骗。在这项研究中,我们提出了一个自动欺骗检测框架,该框架综合了欺骗行为理解中的先验领域知识。具体来说,我们计算了声学、文本信息、非语言行为的含义和会话时间动态,以提高对话中的自动欺骗检测。该模型在普通话每日欺骗对话语料库(DDDM)数据库上达到了最先进的性能,在欺骗识别上的非加权查全率为80.61%。在进一步的分析中,我们发现:(1)从对话时间动态特征中可以观察到欺骗者的欺骗行为;(2)一些声学特征(如响度和MFCC)和文本特征是检测欺骗行为的重要而有效的指标。
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来源期刊
APSIPA Transactions on Signal and Information Processing
APSIPA Transactions on Signal and Information Processing ENGINEERING, ELECTRICAL & ELECTRONIC-
CiteScore
8.60
自引率
6.20%
发文量
30
审稿时长
40 weeks
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