Interpreting Speaker Characteristics in the Dimensions of Self-Supervised Speech Features
The study used Principal Component Analysis (PCA) on utterance-averaged features from self-supervised speech models to understand how speaker characteristics are encoded in individual dimensions. It found that the first principal dimension primarily captures pitch and gender, while other dimensions correlate with intensity, noise levels, and formants. Synthesis experiments confirmed that these dimensions can be manipulated independently.