A Health-friendly Speaker Verification System Supporting Mask Wearing

Chaotao Chen, Di Jiang, Jinhua Peng, Rongzhong Lian, Chen Jason Zhang, Qian Xu, Lixin Fan, Qiang Yang

Research output: Chapter in book / Conference proceedingConference article published in proceeding or bookAcademic researchpeer-review

1 Citation (Scopus)

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.

Original languageEnglish
Title of host publication35th AAAI Conference on Artificial Intelligence, AAAI 2021
PublisherAssociation for the Advancement of Artificial Intelligence
Pages16004-16006
Number of pages3
ISBN (Electronic)9781713835974
Publication statusPublished - 2021
Externally publishedYes
Event35th AAAI Conference on Artificial Intelligence, AAAI 2021 - Virtual, Online
Duration: 2 Feb 20219 Feb 2021

Publication series

Name35th AAAI Conference on Artificial Intelligence, AAAI 2021
Volume18

Conference

Conference35th AAAI Conference on Artificial Intelligence, AAAI 2021
CityVirtual, Online
Period2/02/219/02/21

ASJC Scopus subject areas

  • Artificial Intelligence

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