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Related Experiment Video

Updated: Jul 19, 2025

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STYLETTS-VC: ONE-SHOT VOICE CONVERSION BY KNOWLEDGE TRANSFER FROM STYLE-BASED TTS MODELS.

Yinghao Aaron Li1, Cong Han1, Nima Mesgarani1

  • 1Department of Electrical Engineering, Columbia University, USA.

SLT ... : ... IEEE Workshop on Spoken Language Technology : Proceedings. IEEE Workshop on Spoken Language Technology
|August 14, 2023
PubMed
Summary

This study introduces a new method for one-shot voice conversion (VC) using transfer learning from text-to-speech models. The approach achieves highly realistic speech conversion without needing text input, outperforming existing methods.

Keywords:
Voice conversiondisentangled representationstext-to-speechtransfer learning

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Area of Science:

  • Artificial Intelligence
  • Speech Processing
  • Machine Learning

Background:

  • One-shot voice conversion (VC) enables speech style transfer using minimal target speaker data.
  • Disentangling speaker identity from speech content is a key challenge in VC.
  • Existing methods often require extensive training data or struggle with naturalness.

Purpose of the Study:

  • To develop a novel approach for one-shot voice conversion (VC).
  • To achieve disentangled speech representation for high-fidelity voice transfer.
  • To improve the naturalness and similarity of converted speech.

Main Methods:

  • Utilizing transfer learning from style-based text-to-speech (TTS) models.
  • Employing cycle-consistent and adversarial training for transcription-guided VC.
  • Implementing a teacher-student knowledge transfer with a mel-spectrogram encoder and data augmentation for text-independent representation.

Main Results:

  • The proposed method successfully disentangles speaker identity and speech content.
  • Achieved high fidelity and similarity in one-shot voice conversion.
  • Significantly outperformed state-of-the-art models in subjective evaluations of naturalness and similarity.

Conclusions:

  • The novel approach effectively addresses the challenge of disentangled speech representation in one-shot VC.
  • Transfer learning from TTS models offers a promising direction for advanced voice conversion.
  • The text-independent method enhances the practicality and performance of one-shot VC systems.