TY - JOUR
T1 - Learning domain-heterogeneous speaker recognition systems with personalized continual federated learning
AU - Chen, Zhiyong
AU - Xu, Shugong
N1 - Publisher Copyright:
© 2023, Springer Nature Switzerland AG.
PY - 2023/12
Y1 - 2023/12
N2 - 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.
AB - 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.
KW - Continual learning
KW - Domain adaptation
KW - Federated learning
KW - Speaker recognition
UR - http://www.scopus.com/inward/record.url?scp=85169836357&partnerID=8YFLogxK
U2 - 10.1186/s13636-023-00299-2
DO - 10.1186/s13636-023-00299-2
M3 - Article
AN - SCOPUS:85169836357
SN - 1687-4714
VL - 2023
JO - Eurasip Journal on Audio, Speech, and Music Processing
JF - Eurasip Journal on Audio, Speech, and Music Processing
IS - 1
M1 - 33
ER -