# Disentangled Speech Representation Learning for One-Shot Cross-lingual Voice Conversion Using β-VAE

arXiv: https://arxiv.org/abs/2210.13771 · HTML: https://mlsanity.com/paper/2210.13771/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 25 Oct 2022.

## Built on by (most cited first, 4 of 4)

- [NTU-NPU System for Voice Privacy 2024 Challenge](https://mlsanity.com/paper/2410.02371/) (2024)
- [Voice Cloning: Comprehensive Survey](https://mlsanity.com/paper/2505.00579/) (2025)
- [AdaptVC: High Quality Voice Conversion with Adaptive Learning](https://mlsanity.com/paper/2501.01347/) (2025)
- [VoTSpeech: Decoupling Voice Design from Speech Generation in Instruction-Following TTS](https://mlsanity.com/paper/2610.14694/) (2026)

## Abstract (arXiv)

We propose an unsupervised learning method to disentangle speech into content representation and speaker identity representation. We apply this method to the challenging one-shot cross-lingual voice conversion task to demonstrate the effectiveness of the disentanglement. Inspired by β-VAE, we introduce a learning objective that balances between the information captured by the content and speaker representations. In addition, the inductive biases from the architectural design and the training dataset further encourage the desired disentanglement. Both objective and subjective evaluations show the effectiveness of the proposed method in speech disentanglement and in one-shot cross-lingual voice conversion.