Learning domain-heterogeneous speaker recognition systems with personalized continual federated learning

Zhiyong Chen, Shugong Xu*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

Abstract

Speaker recognition, the process of automatically identifying a speaker based on individual characteristics in speech signals, presents significant challenges when addressing heterogeneous-domain conditions. Federated learning, a recent development in machine learning methods, has gained traction in privacy-sensitive tasks, such as personal voice assistants in home environments. However, its application in heterogeneous multi-domain scenarios for enhancing system customization remains underexplored. In this paper, we propose the utilization of federated learning in heterogeneous situations to enable adaptation across multiple domains. We also introduce a personalized federated learning algorithm designed to effectively leverage limited domain data, resulting in improved learning outcomes. Furthermore, we present a strategy for implementing the federated learning algorithm in practical, real-world continual learning scenarios, demonstrating promising results. The proposed federated learning method exhibits superior performance across a range of synthesized complex conditions and continual learning settings, compared to conventional training methods.

Original languageEnglish
Article number33
JournalEurasip Journal on Audio, Speech, and Music Processing
Volume2023
Issue number1
DOIs
Publication statusPublished - Dec 2023
Externally publishedYes

Keywords

  • Continual learning
  • Domain adaptation
  • Federated learning
  • Speaker recognition

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