Emotional Text-To-Speech (TTS) is an important task in the development of
systems (e.g., human-like dialogue agents) that require natural and emotional
speech. Existing approaches, however, only aim to produce emotional TTS for
seen speakers during training, without consideration of the generalization to
unseen speakers. In this paper, we propose ZET-Speech, a zero-shot adaptive
emotion-controllable TTS model that allows users to synthesize any speaker's
emotional speech using only a short, neutral speech segment and the target
emotion label. Specifically, to enable a zero-shot adaptive TTS model to
synthesize emotional speech, we propose domain adversarial learning and
guidance methods on the diffusion model. Experimental results demonstrate that
ZET-Speech successfully synthesizes natural and emotional speech with the
desired emotion for both seen and unseen speakers. Samples are at
this https URL

本文提出了一种零样本适应情绪可控 TTS 模型 ZET-Speech，旨在实现对任何说话者情感语音的合成，采用了域对抗学习和扩散模型的引导方法，实验结果表明，ZET-Speech 成功地合成了所需情感的自然和情感语音，适用于已知和未知说话者。