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Improved semi-supervised autoencoder for deception detection.

التفاصيل البيبلوغرافية
العنوان: Improved semi-supervised autoencoder for deception detection.
المؤلفون: Hongliang Fu, Peizhi Lei, Huawei Tao, Li Zhao, Jing Yang
المصدر: PLoS ONE, Vol 14, Iss 10, p e0223361 (2019)
بيانات النشر: Public Library of Science (PLoS), 2019.
سنة النشر: 2019
المجموعة: LCC:Medicine
LCC:Science
مصطلحات موضوعية: Medicine, Science
الوصف: Existing algorithms of speech-based deception detection are severely restricted by the lack of sufficient number of labelled data. However, a large amount of easily available unlabelled data has not been utilized in reality. To solve this problem, this paper proposes a semi-supervised additive noise autoencoder model for deception detection. This model updates and optimizes the semi-supervised autoencoder and it consists of two layers of encoder and decoder, and a classifier. Firstly, it changes the activation function of the hidden layer in network according to the characteristics of the deception speech. Secondly, in order to prevent over-fitting during training, the specific ratio dropout is added at each layer cautiously. Finally, we directly connected the supervised classification task in the output of encoder to make the network more concise and efficient. Using the feature set specified by the INTERSPEECH 2009 Emotion Challenge, the experimental results on Columbia-SRI-Colorado (CSC) corpus and our own deception corpus show that the proposed model can achieve more advanced performance than other alternative methods with only a small amount of labelled data.
نوع الوثيقة: article
وصف الملف: electronic resource
اللغة: English
تدمد: 1932-6203
Relation: https://doaj.org/toc/1932-6203
DOI: 10.1371/journal.pone.0223361
URL الوصول: https://doaj.org/article/d27e6a1765d94a97b9d763c87f4d7ff8
رقم الأكسشن: edsdoj.27e6a1765d94a97b9d763c87f4d7ff8
قاعدة البيانات: Directory of Open Access Journals
الوصف
تدمد:19326203
DOI:10.1371/journal.pone.0223361