In this work, we introduce a framework for cross-lingual speech synthesis, which involves an upstream Voice Conversion (VC) model and a downstream Text-To-Speech (TTS) model. The proposed framework consists of 4 stages. In the first two stages, we use a VC model to convert utterances in the target locale to the voice of the target speaker. In the third stage, the converted data is combined with the linguistic features and durations from recordings in the target language, which are then used to train a single-speaker acoustic model. Finally, the last stage entails the training of a locale-independent vocoder. Our evaluations show that the proposed paradigm outperforms state-of-the-art approaches which are based on training a large multilingual TTS model. In addition, our experiments demonstrate the robustness of our approach with different model architectures, languages, speakers and amounts of data. Moreover, our solution is especially beneficial in low-resource settings.

翻译：本文提出了一种跨语言语音合成框架，该框架包含上游语音转换（VC）模型和下游文本转语音（TTS）模型。所提框架由四个阶段组成。在前两个阶段，我们使用语音转换模型将目标语域的语音转换为目标说话人的声音。在第三阶段，转换后的数据与目标语言录音中的语言特征和时长信息相结合，用于训练单说话人声学模型。最后阶段涉及训练一个与语域无关的声码器。实验评估表明，所提范式优于基于训练大型多语言TTS模型的现有最先进方法。此外，我们的实验证明了该方法在不同模型架构、语言、说话人及数据量下均具有鲁棒性。更重要的是，该解决方案在低资源场景中尤其具有优势。