Daily Arxiv

This is a page that curates AI-related papers published worldwide.
All content here is summarized using Google Gemini and operated on a non-profit basis.
Copyright for each paper belongs to the authors and their institutions; please make sure to credit the source when sharing.

Zero-Shot Voice Conversion via Content-Aware Timbre Ensemble and Conditional Flow Matching

Created by
  • Haebom

Author

Yu Pan, Yuguang Yang, Jixun Yao, Lei Ma, Jianjun Zhao

Outline

This paper proposes the Computational Transformation Efficiency Model (CTEFM-VC) framework to address the challenges of securing speaker similarity and naturalness in zero-shot voice conversion (VC). CTEF-VC decomposes speech into content and timbre and reconstructs the Mel spectrogram of the source speech using a conditional flow matching model. Specifically, it introduces context-aware timbre ensemble modeling and a structural similarity-based timbre loss function to enhance the naturalness and timbre modeling performance of the generated speech. A cross-attention module that adaptively integrates various speaker verification embeddings effectively leverages source content and target timbre elements. Experimental results show that CTEFM-VC significantly outperforms existing state-of-the-art zero-shot VC systems, achieving state-of-the-art performance in speaker similarity, naturalness, and intelligibility.

Takeaways, Limitations

Takeaways:
We present a novel framework, CTEFM-VC, that significantly improves speaker similarity and naturalness in zero-shot speech conversion.
Performance enhancement via context-aware timbre ensemble modeling and structural similarity-based timbre loss function.
Effective use of diverse speaker verification embeddings.
Superior performance compared to existing cutting-edge models.
Limitations:
The paper lacks specific references to Limitations or future research directions.
A detailed description of the experimental setup and dataset is required.
There may be a bias towards certain languages or voice data.
👍