Unet-TTS: Improving Unseen Speaker and Style Transfer in One-shot Voice Cloning

TL;DR

Unet-TTS is a novel one-shot voice cloning model that effectively captures speaker and style features from limited references, improving unseen speaker and style transfer in TTS systems.

Contribution

The paper introduces Unet-TTS, a skip-connected U-net based model that enhances generalization for unseen speakers and styles in one-shot voice cloning.

Findings

Outperforms existing methods in similarity evaluations.

Effectively captures complex acoustic features.

Shows strong generalization to unseen speakers and styles.

Abstract

One-shot voice cloning aims to transform speaker voice and speaking style in speech synthesized from a text-to-speech (TTS) system, where only a shot recording from the target reference speech can be used. Out-of-domain transfer is still a challenging task, and one important aspect that impacts the accuracy and similarity of synthetic speech is the conditional representations carrying speaker or style cues extracted from the limited references. In this paper, we present a novel one-shot voice cloning algorithm called Unet-TTS that has good generalization ability for unseen speakers and styles. Based on a skip-connected U-net structure, the new model can efficiently discover speaker-level and utterance-level spectral feature details from the reference audio, enabling accurate inference of complex acoustic characteristics as well as imitation of speaking styles into the synthetic speech. According to both subjective and objective evaluations of similarity, the new model outperforms both speaker embedding and unsupervised style modeling (GST) approaches on an unseen emotional corpus.