Nan Jiang, Ting Liu. Research on Voiceprint Recognition of Camouflage Voice Based on Deep Belief Network. International Journal of Automation and Computing.
Citation: Nan Jiang, Ting Liu.

Research on Voiceprint Recognition of Camouflage Voice Based on Deep Belief Network

. International Journal of Automation and Computing.

Research on Voiceprint Recognition of Camouflage Voice Based on Deep Belief Network

doi: 10.1007/s11633-021-1283-2
More Information
  • Author Bio:

    Nan Jiang received the Ph. D. degree in engineering from Northeastern University, China in 2007, and completed her post-doctoral research in computer science and technology at Northeastern University, China in 2012. She is currently an associate professor in Department of Acoustic and Imaging Information Inspection Technology, Criminal Investigation Police University of China. She is the young talents of criminal science and technology in China, and has been selected into the Millions of Talents Project in Liaoning Province. During her school time, she has been engaged in teaching, scientific research and case handling of judicial voice examination and voice recognition. She has published more than 40 academic papers. Her research interests include pattern recognition, speech recognition, speech emotion recognition, and multimodal emotion recognition. E-mail: ORCID iD: 0000-0002-5497-7007

    Ting Liu received the B. Sc. degree in automation from Northeastern University, China in 2007 and the M. Sc. and Ph. D. degrees in control theory and control engineering from Northeastern University, China in 2009 and 2014, respectively. She is now a lecturer in electrical engineering and automation at Liaoning University, China, and has been engaged in the research of nonlinear algorithms and speech recognition algorithms. Her research interests include pattern recognition, feedback control systems, and control theory. E-mail: (Corresponding author) ORCID iD: 0000-0001-9520-5081

  • Received Date: 2020-08-15
  • Accepted Date: 2021-01-22
  • The problem of disguised voice recognition based on deep belief networks is studied. A hybrid feature extraction algorithm based on formants, Gammatone frequency cepstrum coefficients (GFCC) and their different coefficients is proposed to extract more discriminative speaker features from the original voice data. Using mixed features as the input of the model, a masquerade voice library is constructed. A masquerade voice recognition model based on a depth belief network is proposed. A dropout strategy is introduced to prevent overfitting, which effectively solves the problems of traditional Gaussian mixture models, such as insufficient modeling ability and low discrimination. Experimental results show that the proposed disguised voice recognition method can better fit the feature distribution, and significantly improve the classification effect and recognition rate.


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