We introduce StyleFusion-TTS, a prompt and/or audio referenced, style- and speaker-controllable, zero-shot text-to-speech (TTS) synthesis system designed to enhance the editability and naturalness of current research literature. We propose a general front-end encoder as a compact and effective module to utilize multimodal inputs-including text prompts, audio references, and speaker timbre references-in a fully zero-shot manner and produce disentangled style and speaker control embeddings. Our novel approach also leverages a hierarchical conformer structure for the fusion of style and speaker control embeddings, aiming to achieve optimal feature fusion within the current advanced TTS architecture. StyleFusion-TTS is evaluated through multiple metrics, both subjectively and objectively. The system shows promising performance across our evaluations, suggesting its potential to contribute to the advancement of the field of zero-shot text-to-speech synthesis. A project website provides detailed information for demonstration and reproduction.

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StyleFusion TTS: Multimodal Style-Control and Enhanced Feature Fusion for Zero-Shot Text-to-Speech Synthesis

  • Zhiyong Chen,
  • Xinnuo Li,
  • Zhiqi Ai,
  • Shugong Xu

摘要

We introduce StyleFusion-TTS, a prompt and/or audio referenced, style- and speaker-controllable, zero-shot text-to-speech (TTS) synthesis system designed to enhance the editability and naturalness of current research literature. We propose a general front-end encoder as a compact and effective module to utilize multimodal inputs-including text prompts, audio references, and speaker timbre references-in a fully zero-shot manner and produce disentangled style and speaker control embeddings. Our novel approach also leverages a hierarchical conformer structure for the fusion of style and speaker control embeddings, aiming to achieve optimal feature fusion within the current advanced TTS architecture. StyleFusion-TTS is evaluated through multiple metrics, both subjectively and objectively. The system shows promising performance across our evaluations, suggesting its potential to contribute to the advancement of the field of zero-shot text-to-speech synthesis. A project website provides detailed information for demonstration and reproduction.