Cross-lingual Knowledge Distillation via Flow-based Voice Conversion for Robust Polyglot Text-To-Speech
This addresses the problem of creating robust polyglot text-to-speech systems, especially in low-resource settings, with an incremental improvement over existing methods.
The paper tackles cross-lingual speech synthesis by proposing a framework that uses voice conversion to adapt speech to a target speaker, combined with linguistic features to train acoustic models, resulting in outperforming state-of-the-art multilingual TTS approaches and showing robustness across various settings.
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.