Interpreting Content and Speaker Characteristics in Factorised Self-Supervised Subspaces

Self-supervised speech features encode both content and speaker information. Recent work introduced an SVD-based factorisation that decomposes these features into a shared content matrix capturing temporal variation and speaker-specific transformations capturing static speaker characteristics. However, how information is organised within these components remains unclear. In this paper, we investigate how the dimensions of WavLM-factorised content and speaker subspaces correlate with speech characteristics such as pitch, intensity, and voicing. We find that leading dimensions in the content space primarily capture intensity, higher-order formants, and voicing, while pitch is encoded in a later dimension. In contrast, the highest-variance speaker dimension is strongly associated with pitch and gender, with later dimensions capturing high-frequency variation. Intervention experiments show that manipulating these dimensions enables targeted control of speech characteristics for speech synthesis. Furthermore, modifying the content and speaker representations jointly provides fine-grained control over characteristics such as pitch and intensity.

Interpreting Speaker Characteristics in the Dimensions of Self-Supervised Speech Features

How do speech models trained through self-supervised learning structure their representations? Previous studies have looked at how information is encoded in feature vectors across different layers. But few studies have considered whether speech characteristics are captured within individual dimensions of SSL features. In this paper we specifically look at speaker information using PCA on utterance-averaged representations. Using WavLM, we find that the principal dimension that explains most variance encodes pitch and associated characteristics like gender. Other individual principal dimensions correlate with intensity, noise levels, the second formant, and higher frequency characteristics. Finally, in synthesis experiments we show that most characteristics can be controlled by changing the corresponding dimensions. This provides a simple method to control characteristics of the output voice in synthesis applications.