A Health-friendly Speaker Verification System Supporting Mask Wearing

Authors

  • Chaotao Chen WeBank Co Ltd
  • Di Jiang WeBank Co Ltd
  • Jinhua Peng WeBank Co Ltd
  • Rongzhong Lian WeBank Co Ltd
  • Chen Jason Zhang The Hong Kong University of Science and Technology
  • Qian Xu WeBank Co Ltd
  • Lixin Fan WeBank Co Ltd
  • Qiang Yang The Hong Kong University of Science and Technology

Keywords:

Speaker Verification, Neural Network, Mask Wearing

Abstract

We demonstrate a health-friendly speaker verification system for voice-based identity verification on mobile devices. The system is built upon a speech processing module, a ResNet-based local acoustic feature extractor and a multi-head attention-based embedding layer, and is optimized under an additive margin softmax loss for discriminative speaker verification. It is shown that the system achieves superior performance no matter whether there is mask wearing or not. This characteristic is important for speaker verification services operating in regions affected by the raging coronavirus pneumonia. With this demonstration, the audience will have an in-depth experience of how the accuracy of bio-metric verification and the personal health are simultaneously ensured. We wish that this demonstration would boost the development of next-generation bio-metric verification technologies.

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Published

2021-05-18

How to Cite

Chen, C., Jiang, D., Peng, J., Lian, R., Zhang, C. J., Xu, Q., Fan, L., & Yang, Q. (2021). A Health-friendly Speaker Verification System Supporting Mask Wearing. Proceedings of the AAAI Conference on Artificial Intelligence, 35(18), 16004-16006. Retrieved from https://ojs.aaai.org/index.php/AAAI/article/view/17994