A Comparative Study of Supervised Machine Learning Techniques for Deceptive Review Identification Using Linguistic Inquiry and Word Count

Jayathunga, D. P.; Ranasinghe, R. M. Iranthi Shashikala; Murugiah, Ramashini

doi:10.1007/978-3-030-68133-3_10

Cited by 5 publications

(1 citation statement)

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“…Currently, lie detection leverages a spectrum of signals, including speech, facial expressions, and other physiological indicators [3,4]. In the realm of speech-based detection methods, employing psycholinguistic features based on language inquiry has been proposed as an effective means for lie detection [5]. Studies have shown that the pitch, duration, energy, and pauses during speech can provide information on lying [6][7][8].…”

Section: Introductionmentioning

confidence: 99%

Lie Detection Technology of Bimodal Feature Fusion Based on Domain Adversarial Neural Networks

Zhou,

2024

IET Signal Processing

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In the domain of lie detection, a common challenge arises from the dissimilar distributions of training and testing datasets. This causes a model mismatch, leading to a performance decline of the pretrained deep learning model. To solve this problem, we propose a lie detection technique based on a domain adversarial neural network employing a dual-mode state feature. First, a deep learning neural network was used as a feature extractor to isolate speech and facial expression features exhibited by the liars. The data distributions of the source and target domain signals must be aligned. Second, a domain-antagonistic transfer-learning mechanism is introduced to build a neural network. The objective is to facilitate feature migration from the training to the testing domain, that is, the migration of lie-related features from the source to the target domain. This method results in improved lie detection accuracy. Simulations conducted on two professional lying databases with different distributions show the superiority of the detection rate of the proposed method compared to an unimodal feature detection algorithm. The maximum improvement in detection rate was 23.3% compared to the traditional neural network-based detection method. Therefore, the proposed method can learn features unrelated to domain categories, effectively mitigating the problem posed by different distributions in the training and testing of lying data.

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