Deception detecting from speech signal using relevance vector machine and non-linear dynamics features

作者：Zhou, Yan^*; Zhao, Heming; Pan, Xinyu; Shang, Li

来源：Neurocomputing, 2015, 151: 1042-1052.

DOI：10.1016/j.neucom.2014.04.083

• Deception detecting
• Speech signal
• Relevance vector machine (RVM)
• Sparse bayesian learning (SBL)
• Non-linear dynamics (NLD)
• Signal to noise ratio (SNR)

The novel method of deception detecting based on speech signal is proposed in this study. Extracting prosodic and non-linear dynamics (NLD) feature sets from speech signal and applying relevance vector machine (RVM) classification method is the primary target of this paper. Here, the sustained speaker-depended phonation samples of deception and non-deception were applied. In this paper, 30 prosodic features and 18 NLD features were selected which show significant correlations to deception state. Moreover, the RVM classification model based on sparse bayesian learning (SBL) was introduced which is a bayesian extension of the support vector machine (SVM) and not be restricted by the Mercer's condition. In the experiments, the deception corpus of Soochow University is exploited to test our approach. Firstly, the experiment of feature performance test was carried out. It is demonstrated that the combination of prosodic and NLD features is the most effectively method for detecting deceptive speech. Secondly, the property of RVM classification model is measured. The experiment results show that RVM technology requires much fewer basic functions and demands much less decision time than the SVM algorithm. Furthermore, during the test of correct classification accuracy of RVM, it shows that RVM is much higher than the classical model of SVM and radial basis function neural network (RBFNN). Finally, the robustness test of RVM is processed and received better performance than SVM and RBFNN methods when the signal to noise ratio (SNR) falls. In general, the proposed deception detecting method based on the combined features and RVM classifier is novel, convenient and practical, moreover, it achieves higher classification accuracy, less detection time, property of generalization, and the strong robustness.

• 出版日期2015-3-3
• 单位苏州市职业大学; 中国人民解放军信息工程大学; 苏州大学