Privacy and Utility of X-Vector Based Speaker Anonymization

We study the scenario where individuals ( speakers ) contribute to the publication of an anonymized speech corpus. Data users leverage this public corpus for downstream tasks, e.g., training an automatic speech recognition (ASR) system, while attackers may attempt to de-anonymize it using auxiliary...

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Vydané v:	IEEE/ACM transactions on audio, speech, and language processing Ročník 30; s. 2383 - 2395
Hlavní autori:	Srivastava, Brij Mohan Lal, Maouche, Mohamed, Sahidullah, Md, Vincent, Emmanuel, Bellet, Aurelien, Tommasi, Marc, Tomashenko, Natalia, Wang, Xin, Yamagishi, Junichi
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	Piscataway IEEE 01.01.2022 The Institute of Electrical and Electronics Engineers, Inc. (IEEE) Institute of Electrical and Electronics Engineers
Predmet:	Artificial Intelligence Audio equipment Automatic speech recognition Cloning Computation and Language Computer Science Corpus linguistics Error analysis Feature extraction Linkability Machine Learning Measurement Privacy Quality assessment speaker anonymization Speech recognition Speech synthesis Task analysis Training voice conversion Voice recognition linkability privacy utility speech recognition speaker identification speaker anonymization
ISSN:	2329-9290, 2329-9304
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