Adversarial Training for Multi-domain Speaker Recognition

Qing Wang, Wei Rao, Pengcheng Guo, Lei Xie

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

11 Scopus citations

Abstract

In real-life applications, the performance of speaker recognition systems always degrades when there is a mismatch between training and evaluation data. Many domain adaptation methods have been successfully used for eliminating the domain mismatches in speaker recognition. However, usually both training and evaluation data themselves can be composed of several subsets. These inner variances of each dataset can also be considered as different domains. Different distributed subsets in source or target domain dataset can also cause multi-domain mismatches, which are influential to speaker recognition performance. In this study, we propose to use adversarial training for multi-domain speaker recognition to solve the domain mismatch and the dataset variance problems. By adopting the proposed method, we are able to obtain both multi-domain-invariant and speaker-discriminative speech representations for speaker recognition. Experimental results on DAC13 dataset indicate that the proposed method is not only effective to solve the multi-domain mismatch problem, but also outperforms the compared unsupervised domain adaptation methods.

Original languageEnglish
Title of host publication2021 12th International Symposium on Chinese Spoken Language Processing, ISCSLP 2021
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781728169941
DOIs
StatePublished - 24 Jan 2021
Event12th International Symposium on Chinese Spoken Language Processing, ISCSLP 2021 - Hong Kong, Hong Kong
Duration: 24 Jan 202127 Jan 2021

Publication series

Name2021 12th International Symposium on Chinese Spoken Language Processing, ISCSLP 2021

Conference

Conference12th International Symposium on Chinese Spoken Language Processing, ISCSLP 2021
Country/TerritoryHong Kong
CityHong Kong
Period24/01/2127/01/21

Keywords

  • adversarial training
  • multi-domain adaptation
  • speaker recognition

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