Self-supervised speech representations are known to encode both speaker and
phonetic information, but how they are distributed in the high-dimensional
space remains largely unexplored. We hypothesize that they are encoded in
orthogonal subspaces, a property that lends itself to simple disentanglement.
Applying principal component analysis to representations of two predictive
coding models, we identify two subspaces that capture speaker and phonetic
variances, and confirm that they are nearly orthogonal. Based on this property,
we propose a new speaker normalization method which collapses the subspace that
encodes speaker information, without requiring transcriptions. Probing
experiments show that our method effectively eliminates speaker information and
outperforms a previous baseline in phone discrimination tasks. Moreover, the
approach generalizes and can be used to remove information of unseen speakers.

本文研究自监督语音表示的特征空间分布，通过主成分分析得出编码说话者和语音的正交子空间，提出一种新的说话者标准化方法，消除说话者信息，对于去除语音中的说话者信息的任务表现出色。