Abstract
Lie speech detection is a typical psychological calculation problem. As the lie information is hidden in speech flow and cannot be easily found, so lie speech is a complex research object. Lie speech detection is not only need to pay attention to the surface information such as words, symbols and sentence, it is more important to pay attention to the internal essence structure characteristics. Therefore, based on the study of speech signal sparse representation, this paper proposes a Stack Sparse Automatic Encoder (SSAE) deep learning model for lying speech characteristics extraction. The proposed method is an effective one, it can reflect people’s deep lying characteristics, and weaken lying person’s personality traits. The deep characteristics compensate the lack of lie expression of basic acoustic features. This improved the lying state correct recognition rate. The experimental results show that, due to the introduction of deep learning characteristics, the individual lying recognition rate has increased by 4%–10%. This result suggests that, the lie detection based on speech analysis method is feasible. Furthermore, the proposed lying state detection based on speech characteristic provides a new research way of psychological calculation.
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Acknowledgments
This research is supported by: National Natural Science Foundations of China (Grant Nos. 61372146, 61373098). The Youth Found of Natural Science Foundation of Jiangsu Province of China (Grant No. BK20160361). The fund of Qinglan Project Young and Middle-aged Academic Leader of Jiangsu Province.
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Zhou, Y., Zhao, H., Shang, L. (2017). Lying Speech Characteristic Extraction Based on SSAE Deep Learning Model. In: Huang, DS., Hussain, A., Han, K., Gromiha, M. (eds) Intelligent Computing Methodologies. ICIC 2017. Lecture Notes in Computer Science(), vol 10363. Springer, Cham. https://doi.org/10.1007/978-3-319-63315-2_59
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DOI: https://doi.org/10.1007/978-3-319-63315-2_59
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