AdaptVC: High Quality Voice Conversion with Adaptive Learning

• URL: http://arxiv.org/abs/2501.01347v4
• Date: Tue, 14 Jan 2025 11:36:42 GMT
• Title: AdaptVC: High Quality Voice Conversion with Adaptive Learning
• Authors: Jaehun Kim, Ji-Hoon Kim, Yeunju Choi, Tan Dat Nguyen, Seongkyu Mun, Joon Son Chung,
• Abstract summary: Key challenge is to extract disentangled linguistic content from the source and voice style from the reference. In this paper, we achieve successful disentanglement of content and speaker features by tuning self-supervised speech features with adapters. The adapters are trained to dynamically encode nuanced features from rich self-supervised features, and the decoder fuses them to produce speech that accurately resembles the reference.
• Score: 28.25726543043742
• License:
• Abstract: The goal of voice conversion is to transform the speech of a source speaker to sound like that of a reference speaker while preserving the original content. A key challenge is to extract disentangled linguistic content from the source and voice style from the reference. While existing approaches leverage various methods to isolate the two, a generalization still requires further attention, especially for robustness in zero-shot scenarios. In this paper, we achieve successful disentanglement of content and speaker features by tuning self-supervised speech features with adapters. The adapters are trained to dynamically encode nuanced features from rich self-supervised features, and the decoder fuses them to produce speech that accurately resembles the reference with minimal loss of content. Moreover, we leverage a conditional flow matching decoder with cross-attention speaker conditioning to further boost the synthesis quality and efficiency. Subjective and objective evaluations in a zero-shot scenario demonstrate that the proposed method outperforms existing models in speech quality and similarity to the reference speech.

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