Voice anonymization has been developed as a technique for preserving privacy
by replacing the speaker's voice in a speech signal with that of a
pseudo-speaker, thereby obscuring the original voice attributes from machine
recognition and human perception. In this paper, we focus on altering the voice
attributes against machine recognition while retaining human perception. We
referred to this as the asynchronous voice anonymization. To this end, a speech
generation framework incorporating a speaker disentanglement mechanism is
employed to generate the anonymized speech. The speaker attributes are altered
through adversarial perturbation applied on the speaker embedding, while human
perception is preserved by controlling the intensity of perturbation.
Experiments conducted on the LibriSpeech dataset showed that the speaker
attributes were obscured with their human perception preserved for 60.71% of
the processed utterances.

本文研究声音匿名化技术，重点探讨改变语音特征以防止机器识别但保留人类感知的异步声音匿名化方法，采用包含说话人解缠机制的语音生成框架生成匿名语音，并通过对说话人嵌入进行对抗扰动改变说话人特征，同时通过控制扰动强度保留人类感知。实验结果表明，在 LibriSpeech 数据集上处理的 utterances 中，60.71％的处理结果达到了说话人特征的匿名化且保留了人类感知。

基于对说话人嵌入进行对抗扰动的异步语音匿名化

Asynchronous Voice Anonymization Using Adversarial Perturbation On  Speaker Embedding

Self-supervised learning in speech involves training a speech representation
network on a large-scale unannotated speech corpus, and then applying the
learned representations to downstream tasks. Since the majority of the
downstream tasks of SSL learning in speech largely focus on the content
information in speech, the most desirable speech representations should be able
to disentangle unwanted variations, such as speaker variations, from the
content. However, disentangling speakers is very challenging, because removing
the speaker information could easily result in a loss of content as well, and
the damage of the latter usually far outweighs the benefit of the former. In
this paper, we propose a new SSL method that can achieve speaker
disentanglement without severe loss of content. Our approach is adapted from
the HuBERT framework, and incorporates disentangling mechanisms to regularize
both the teacher labels and the learned representations. We evaluate the
benefit of speaker disentanglement on a set of content-related downstream
tasks, and observe a consistent and notable performance advantage of our
speaker-disentangled representations.

本文介绍了一种新的自监督学习方法，采用 HuBERT 框架并结合分离机制，能够在不丢失语音信息的前提下实现说话人分离，并在内容相关的下游任务中获得显著的性能提升。