{"title":"The VoicePrivacy 2022 Challenge: Progress and Perspectives in Voice Anonymisation","authors":"Michele Panariello;Natalia Tomashenko;Xin Wang;Xiaoxiao Miao;Pierre Champion;Hubert Nourtel;Massimiliano Todisco;Nicholas Evans;Emmanuel Vincent;Junichi Yamagishi","doi":"10.1109/TASLP.2024.3430530","DOIUrl":null,"url":null,"abstract":"The VoicePrivacy Challenge promotes the development of voice anonymisation solutions for speech technology. In this paper we present a systematic overview and analysis of the second edition held in 2022. We describe the voice anonymisation task and datasets used for system development and evaluation, present the different attack models used for evaluation, and the associated objective and subjective metrics. We describe three anonymisation baselines, provide a summary description of the anonymisation systems developed by challenge participants, and report objective and subjective evaluation results for all. In addition, we describe post-evaluation analyses and a summary of related work reported in the open literature. Results show that solutions based on voice conversion better preserve utility, that an alternative which combines automatic speech recognition with synthesis achieves greater privacy, and that a privacy-utility trade-off remains inherent to current anonymisation solutions. Finally, we present our ideas and priorities for future VoicePrivacy Challenge editions.","PeriodicalId":13332,"journal":{"name":"IEEE/ACM Transactions on Audio, Speech, and Language Processing","volume":"32 ","pages":"3477-3491"},"PeriodicalIF":4.1000,"publicationDate":"2024-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"IEEE/ACM Transactions on Audio, Speech, and Language Processing","FirstCategoryId":"94","ListUrlMain":"https://ieeexplore.ieee.org/document/10603395/","RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"ACOUSTICS","Score":null,"Total":0}
引用次数: 0
Abstract
The VoicePrivacy Challenge promotes the development of voice anonymisation solutions for speech technology. In this paper we present a systematic overview and analysis of the second edition held in 2022. We describe the voice anonymisation task and datasets used for system development and evaluation, present the different attack models used for evaluation, and the associated objective and subjective metrics. We describe three anonymisation baselines, provide a summary description of the anonymisation systems developed by challenge participants, and report objective and subjective evaluation results for all. In addition, we describe post-evaluation analyses and a summary of related work reported in the open literature. Results show that solutions based on voice conversion better preserve utility, that an alternative which combines automatic speech recognition with synthesis achieves greater privacy, and that a privacy-utility trade-off remains inherent to current anonymisation solutions. Finally, we present our ideas and priorities for future VoicePrivacy Challenge editions.
期刊介绍:
The IEEE/ACM Transactions on Audio, Speech, and Language Processing covers audio, speech and language processing and the sciences that support them. In audio processing: transducers, room acoustics, active sound control, human audition, analysis/synthesis/coding of music, and consumer audio. In speech processing: areas such as speech analysis, synthesis, coding, speech and speaker recognition, speech production and perception, and speech enhancement. In language processing: speech and text analysis, understanding, generation, dialog management, translation, summarization, question answering and document indexing and retrieval, as well as general language modeling.