Accent-VITS: Accent Transfer for End-to-End TTS

Accent transfer aims to transfer an accent from a source speaker to synthetic speech in the target speaker’s voice. The main challenge is how to effectively disentangle speaker timbre and accent which are entangled in speech. This paper presents a VITS-based [7] end-to-end accent transfer model named Accent-VITS. Based on the main structure of VITS, Accent-VITS makes substantial improvements to enable effective and stable accent transfer. We leverage a hierarchical CVAE structure to model accent pronunciation information and acoustic features, respectively, using bottleneck features and mel spectrums as constraints. Moreover, the text-to-wave mapping in VITS is decomposed into text-to-accent and accent-to-wave mappings in Accent-VITS. In this way, the disentanglement of accent and speaker timbre becomes be more stable and effective. Experiments on multi-accent and Mandarin datasets show that Accent-VITS achieves higher speaker similarity, accent similarity and speech naturalness as compared with a strong baseline (Demos: https://anonymous-accentvits.github.io/AccentVITS/ ).