Journal of Computer Applications ›› 2020, Vol. 40 ›› Issue (2): 589-594.DOI: 10.11772/j.issn.1001-9081.2019071183

• Frontier & interdisciplinary applications • Previous Articles     Next Articles

Speech deception detection algorithm based on denoising autoencoder and long short-term memory network

Hongliang FU, Peizhi LEI()   

  1. School of Information Science and Engineering,Henan University of Technology,Zhengzhou 450001,China
  • Received:2019-07-08 Revised:2019-09-01 Accepted:2019-09-02 Online:2019-09-19 Published:2020-02-10
  • Contact: Peizhi LEI
  • About author:FU Hongliang, born in 1965, Ph. D., professor. His research interests include modern signal processing.
  • Supported by:
    the National Natural Science Foundation of China(61601170)


傅洪亮, 雷沛之()   

  1. 河南工业大学 信息科学与工程学院,郑州 450001
  • 通讯作者: 雷沛之
  • 作者简介:傅洪亮(1965—),男,河南郑州人,教授,博士,主要研究方向:现代信号处理;
  • 基金资助:


In order to further improve the performance of speech deception detection, a speech deception detection algorithm based on Denoising AutoEncoder (DAE) and Long Short-Term Memory (LSTM) network was proposed. Firstly, a parallel structure of DAE and LSTM was constructed, namely PDL (Parallel connection of DAE and LSTM). Then, artificial features in the speech were extracted and put into the DAE to obtain more robust features. Simultaneously, the Mel spectrums extracted after adding windows to the speech and framing were input into LSTM frame-by-frame for frame-level depth feature learning. Finally, these two types of features were merged by the fully connected layer and the batch normalization, and the softmax classifier was used for the deception recognition. The experimental results on the CSC (Columbia-SRI-Colorado) corpus and the self-built corpus show that the recognition accuracy of the classification with fusion feature is 65.18% and 68.04% respectively, which is up to 5.56% and 7.22% higher than those of other algorithms, indicating that the proposed algorithm can effectively improve the accuracy of deception recognition.

Key words: Denoising AutoEncoder (DAE), Long Short-Term Memory (LSTM) network, speech feature, feature fusion, deception detection



关键词: 去噪自编码器, 长短时记忆网络, 语音特征, 特征融合, 测谎

CLC Number: