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。