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

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 tr...
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Autor*in:	Chen, Zhiyong [verfasserIn] Xu, Shugong

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2023

Schlagwörter:	Speaker recognition Federated learning Domain adaptation Continual learning

Anmerkung:	© The Author(s) 2023

Übergeordnetes Werk:	Enthalten in: EURASIP Journal on audio, speech, and music processing - Heidelberg : Springer, 2006, 2023(2023), 1 vom: 05. Sept.
Übergeordnetes Werk:	volume:2023 ; year:2023 ; number:1 ; day:05 ; month:09

Links:	Volltext

DOI / URN:	10.1186/s13636-023-00299-2

Katalog-ID:	SPR052995534

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allfieldsGer	10.1186/s13636-023-00299-2 doi (DE-627)SPR052995534 (SPR)s13636-023-00299-2-e DE-627 ger DE-627 rakwb eng Chen, Zhiyong verfasserin (orcid)0000-0002-9629-6111 aut Learning domain-heterogeneous speaker recognition systems with personalized continual federated learning 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) 2023 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. Speaker recognition (dpeaa)DE-He213 Federated learning (dpeaa)DE-He213 Domain adaptation (dpeaa)DE-He213 Continual learning (dpeaa)DE-He213 Xu, Shugong aut Enthalten in EURASIP Journal on audio, speech, and music processing Heidelberg : Springer, 2006 2023(2023), 1 vom: 05. Sept. (DE-627)518348229 (DE-600)2252877-5 1687-4722 nnns volume:2023 year:2023 number:1 day:05 month:09 https://dx.doi.org/10.1186/s13636-023-00299-2 kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2108 GBV_ILN_2119 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 2023 2023 1 05 09
allfieldsSound	10.1186/s13636-023-00299-2 doi (DE-627)SPR052995534 (SPR)s13636-023-00299-2-e DE-627 ger DE-627 rakwb eng Chen, Zhiyong verfasserin (orcid)0000-0002-9629-6111 aut Learning domain-heterogeneous speaker recognition systems with personalized continual federated learning 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) 2023 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. Speaker recognition (dpeaa)DE-He213 Federated learning (dpeaa)DE-He213 Domain adaptation (dpeaa)DE-He213 Continual learning (dpeaa)DE-He213 Xu, Shugong aut Enthalten in EURASIP Journal on audio, speech, and music processing Heidelberg : Springer, 2006 2023(2023), 1 vom: 05. Sept. (DE-627)518348229 (DE-600)2252877-5 1687-4722 nnns volume:2023 year:2023 number:1 day:05 month:09 https://dx.doi.org/10.1186/s13636-023-00299-2 kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2108 GBV_ILN_2119 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 2023 2023 1 05 09
language	English
source	Enthalten in EURASIP Journal on audio, speech, and music processing 2023(2023), 1 vom: 05. Sept. volume:2023 year:2023 number:1 day:05 month:09
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topic_facet	Speaker recognition Federated learning Domain adaptation Continual learning
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authorswithroles_txt_mv	Chen, Zhiyong @@aut@@ Xu, Shugong @@aut@@
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author	Chen, Zhiyong
spellingShingle	Chen, Zhiyong misc Speaker recognition misc Federated learning misc Domain adaptation misc Continual learning Learning domain-heterogeneous speaker recognition systems with personalized continual federated learning
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topic_title	Learning domain-heterogeneous speaker recognition systems with personalized continual federated learning Speaker recognition (dpeaa)DE-He213 Federated learning (dpeaa)DE-He213 Domain adaptation (dpeaa)DE-He213 Continual learning (dpeaa)DE-He213
topic	misc Speaker recognition misc Federated learning misc Domain adaptation misc Continual learning
topic_unstemmed	misc Speaker recognition misc Federated learning misc Domain adaptation misc Continual learning
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title	Learning domain-heterogeneous speaker recognition systems with personalized continual federated learning
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title_full	Learning domain-heterogeneous speaker recognition systems with personalized continual federated learning
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title_sort	learning domain-heterogeneous speaker recognition systems with personalized continual federated learning
title_auth	Learning domain-heterogeneous speaker recognition systems with personalized continual federated learning
abstract	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. © The Author(s) 2023
abstractGer	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. © The Author(s) 2023
abstract_unstemmed	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. © The Author(s) 2023
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title_short	Learning domain-heterogeneous speaker recognition systems with personalized continual federated learning
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