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arXiv · 2411.19770

Noro: Noise-Robust One-shot Voice Conversion with Hidden Speaker Representation Learning

Haorui He·Yuchen Song·Yuancheng Wang·Haoyang Li·Xueyao Zhang·Li Wang·Gongping Huang·Eng Siong Chng·Zhizheng Wu
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Abstract

The effectiveness of one-shot voice conversion (VC) decreases in real-world scenarios where reference speeches, which are often sourced from the internet, contain various disturbances like background noise. To address this issue, we introduce Noro, a noise-robust one-shot VC system. Noro features innovative components tailored for VC using noisy reference speeches, including a dual-branch reference encoding module and a noise-agnostic contrastive speaker loss. Experimental results demonstrate that Noro outperforms our baseline system in both clean and noisy scenarios, highlighting its efficacy for real-world applications. Additionally, we investigate the hidden speaker representation capabilities of our baseline system by repurposing its reference encoder as a speaker encoder. The results show that it is competitive with several advanced self-supervised learning models for speaker representation under the SUPERB settings, highlighting the potential for advancing speaker representation learning through one-shot VC tasks.

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Haorui He, Yuchen Song, Yuancheng Wang, Haoyang Li, Xueyao Zhang, Li Wang, Gongping Huang, Eng Siong Chng, Zhizheng Wu. 2024-11-29. Noro: Noise-Robust One-shot Voice Conversion with Hidden Speaker Representation Learning. https://arxiv.org/abs/2411.19770

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