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.

翻译：自监督语音表征已知同时编码说话人与语音信息，但这些信息在高维空间中的分布方式仍未被充分探索。我们假设它们被编码在正交子空间中，这一特性有利于实现简单解耦。通过对两种预测编码模型的表征应用主成分分析，我们识别出两个分别捕捉说话人差异和语音差异的子空间，并证实它们近乎正交。基于该特性，我们提出一种无需转录文本的新型说话人归一化方法——该方法通过坍缩编码说话人信息的子空间实现。探测实验表明，我们的方法能有效消除说话人信息，并在音素区分任务中优于先前基线方法。此外，该方法具有泛化能力，可用于消除未见说话人的信息。