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Recognition of Voiceprint Using Deep Neural Network Combined with Support Vector Machine

  • Lv Han
  • Tianxing Li
  • Weijie Zheng
  • Tao Ma
  • Wenlian Ma
  • Sanzhi Shi
  • Xiaoning Jia
  • Linhua ZhouEmail author
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 1084)

Abstract

With the rapid development of artificial intelligence technology of deep learning method, it has been applied to many fields, especially to life science. In this paper, a novel approach for the task of voiceprint recognition was proposed. The combination of deep belief network (DBN) and support vector machine (SVM) was used to identify the voiceprint of 10 different individuals. Based on a 24-dimension Mel Frequency Cepstrum Coefficient (MFCC), the authors extracted 256-dimension deep voiceprint features via DBN model that is developed by stacking three layers of Restricted Boltzmann Machine (RBM), and conducted voiceprint recognition by approach of SVM. According to the recognition results, the new approach can significantly improve both of accuracy and efficiency when it was compared with traditional voiceprint features and recognition models. The low dimensional features of voiceprint was extracted into higher dimensions by DBN model, while SVM can avoid the elevation of computation complexity caused by the increases of feature dimension. The combined strengths have been fully expressed by the experimental results.

Keywords

Voiceprint recognition Deep Neural Network Restricted Boltzmann Machine Support vector machine 

Notes

Acknowledgments

This work was partially supported by the National Natural Science Foundation of P. R. China (No. 11401092, 11426045), Scientific and Technological Planning Project of Ji Lin Province of P. R. China (No. 20180101229JC), Foundation of Ji Lin Educational Committee (No. JJKH20181100KJ).

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Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  • Lv Han
    • 1
  • Tianxing Li
    • 1
  • Weijie Zheng
    • 1
  • Tao Ma
    • 1
  • Wenlian Ma
    • 1
  • Sanzhi Shi
    • 1
  • Xiaoning Jia
    • 1
  • Linhua Zhou
    • 1
    Email author
  1. 1.School of Science, Department of Applied MathematicsChangchun University of Science and TechnologyChangchunChina

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