Do WaveNets Dream of Acoustic Waves?
This provides interpretability insights for speech generation models, addressing a gap in understanding how WaveNets process acoustic features.
The study investigated whether WaveNet learns acoustically meaningful latent representations from speech signals, finding that higher-layer activations correlate with spectral features and that WaveNet explicitly extracts pitch despite being trained for sample prediction.
Various sources have reported the WaveNet deep learning architecture being able to generate high-quality speech, but to our knowledge there haven't been studies on the interpretation or visualization of trained WaveNets. This study investigates the possibility that WaveNet understands speech by unsupervisedly learning an acoustically meaningful latent representation of the speech signals in its receptive field; we also attempt to interpret the mechanism by which the feature extraction is performed. Suggested by singular value decomposition and linear regression analysis on the activations and known acoustic features (e.g. F0), the key findings are (1) activations in the higher layers are highly correlated with spectral features; (2) WaveNet explicitly performs pitch extraction despite being trained to directly predict the next audio sample and (3) for the said feature analysis to take place, the latent signal representation is converted back and forth between baseband and wideband components.