Multi-Speaker Multi-Style Speech Synthesis with Timbre and Style Disentanglement

TL;DR

This paper introduces a simple, effective method for disentangling speaker timbre and style in multi-speaker TTS, enabling expressive speech synthesis with high style and speaker similarity without requiring complex data or models.

Contribution

It proposes a novel disentanglement approach using FastSpeech2 with explicit style representations, removing the need for single-speaker multi-style data and simplifying training.

Findings

High style similarity in synthesized speech

Maintains very high speaker similarity

Effective disentanglement of timbre and style

Abstract

Disentanglement of a speaker's timbre and style is very important for style transfer in multi-speaker multi-style text-to-speech (TTS) scenarios. With the disentanglement of timbres and styles, TTS systems could synthesize expressive speech for a given speaker with any style which has been seen in the training corpus. However, there are still some shortcomings with the current research on timbre and style disentanglement. The current method either requires single-speaker multi-style recordings, which are difficult and expensive to collect, or uses a complex network and complicated training method, which is difficult to reproduce and control the style transfer behavior. To improve the disentanglement effectiveness of timbres and styles, and to remove the reliance on single-speaker multi-style corpus, a simple but effective timbre and style disentanglement method is proposed in this paper. The FastSpeech2 network is employed as the backbone network, with explicit duration, pitch, and energy trajectory to represent the style. Each speaker's data is considered as a separate and isolated style, then a speaker embedding and a style embedding are added to the FastSpeech2 network to learn disentangled representations. Utterance level pitch and energy normalization are utilized to improve the decoupling effect. Experimental results demonstrate that the proposed model could synthesize speech with any style seen during training with high style similarity while maintaining very high speaker similarity.