SpeechTripleNet: End-to-End Disentangled Speech Representation Learning for Content, Timbre and Prosody

Disentangled speech representation learning aims to separate different factors of variation from speech into disjoint representations. This paper focuses on disentangling speech into representations for three factors: spoken content, speaker timbre, and speech prosody. Many previous methods for speech disentanglement have focused on separating spoken content and speaker timbre. However, the lack of explicit modeling of prosodic information leads to degraded speech generation performance and uncontrollable prosody leakage into content and/or speaker representations. While some recent methods have utilized explicit speaker labels or pre-trained models to facilitate triple-factor disentanglement, there are no end-to-end methods to simultaneously disentangle three factors using only unsupervised or self-supervised learning objectives. This paper introduces SpeechTripleNet, an end-to-end method to disentangle speech into representations for content, timbre, and prosody. Based on VAE, SpeechTripleNet restricts the structures of the latent variables and the amount of information captured in them to induce disentanglement. It is a pure unsupervised/self-supervised learning method that only requires speech data and no additional labels. Our qualitative and quantitative results demonstrate that SpeechTripleNet is effective in achieving triple-factor speech disentanglement, as well as controllable speech editing concerning different factors.