This paper presents a method for end-to-end cross-lingual text-to-speech
(TTS) which aims to preserve the target language's pronunciation regardless of
the original speaker's language. The model used is based on a non-attentive
Tacotron architecture, where the decoder has been replaced with a normalizing
flow network conditioned on the speaker identity, allowing both TTS and voice
conversion (VC) to be performed by the same model due to the inherent
linguistic content and speaker identity disentanglement. When used in a
cross-lingual setting, acoustic features are initially produced with a native
speaker of the target language and then voice conversion is applied by the same
model in order to convert these features to the target speaker's voice. We
verify through objective and subjective evaluations that our method can have
benefits compared to baseline cross-lingual synthesis. By including speakers
averaging 7.5 minutes of speech, we also present positive results on
low-resource scenarios.

该论文介绍了一种端到端的跨语言文字转语音方法，使用基于非注意力 Tacotron 架构的模型，并通过使用条件为说话人身份的归一化流网络，实现 TTS 和语音转换（VC）的可同时进行，该方法可以在低资源情景下获得良好效果。