For new participants - Executive summary: (1) The task is to develop a voice
anonymization system for speech data which conceals the speaker's voice
identity while protecting linguistic content, paralinguistic attributes,
intelligibility and naturalness. (2) Training, development and evaluation
datasets are provided in addition to 3 different baseline anonymization
systems, evaluation scripts, and metrics. Participants apply their developed
anonymization systems, run evaluation scripts and submit objective evaluation
results and anonymized speech data to the organizers. (3) Results will be
presented at a workshop held in conjunction with INTERSPEECH 2022 to which all
participants are invited to present their challenge systems and to submit
additional workshop papers.
For readers familiar with the VoicePrivacy Challenge - Changes w.r.t. 2020:
(1) A stronger, semi-informed attack model in the form of an automatic speaker
verification (ASV) system trained on anonymized (per-utterance) speech data.
(2) Complementary metrics comprising the equal error rate (EER) as a privacy
metric, the word error rate (WER) as a primary utility metric, and the pitch
correlation and gain of voice distinctiveness as secondary utility metrics. (3)
A new ranking policy based upon a set of minimum target privacy requirements.

该研究论文旨在研发一种语音匿名化系统，以隐藏说话者的身份并保护语言内容，语用属性，可理解度和自然度。研究提供数据集，基础匿名化系统，评估脚本和度量标准，并邀请参与者提交他们的匿名化系统并提交匿名化语音数据以进行评估。