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arXiv · 2507.09070

SemAlignVC: Enhancing zero-shot timbre conversion using semantic alignment

Shivam Mehta·Yingru Liu·Zhenyu Tang·Kainan Peng·Vimal Manohar·Shun Zhang·Mike Seltzer·Qing He·Mingbo Ma
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Abstract

Zero-shot voice conversion (VC) synthesizes speech in a target speaker's voice while preserving linguistic and paralinguistic content. However, timbre leakage-where source speaker traits persist-remains a challenge, especially in neural codec and LLM-based VC, where quantized representations entangle speaker identity with content. We introduce SemAlignVC, an architecture designed to prevent timbre leakage using SemAlign, a novel method that aligns text and audio representations to ensure speaker-independent semantic encoding. This disentangled representation conditions an autoregressive transformer for high-fidelity conversion without explicit speaker embeddings. Experiments show SemAlignVC significantly reduces timbre leakage, outperforming baselines in speaker timbre similarity, intelligibility, and naturalness, making it a robust, privacy-preserving, and generalizable VC solution. Audio samples can be accessed at https://shivammehta25.github.io/SemAlignVC/

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Shivam Mehta, Yingru Liu, Zhenyu Tang, Kainan Peng, Vimal Manohar, Shun Zhang, Mike Seltzer, Qing He, Mingbo Ma. 2025-07-11. SemAlignVC: Enhancing zero-shot timbre conversion using semantic alignment. https://arxiv.org/abs/2507.09070

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