VoicePrompter: Robust Zero-Shot Voice Conversion with Voice Prompt and Conditional Flow Matching

• URL: http://arxiv.org/abs/2501.17612v1
• Date: Wed, 29 Jan 2025 12:34:58 GMT
• Title: VoicePrompter: Robust Zero-Shot Voice Conversion with Voice Prompt and Conditional Flow Matching
• Authors: Ha-Yeong Choi, Jaehan Park,
• Abstract summary: VoicePrompter is a robust zero-shot voice conversion model that leverages in-context learning with voice prompts. We show that VoicePrompter outperforms existing zero-shot VC systems in terms of speaker similarity, speech intelligibility, and audio quality.
• Score: 0.7366405857677227
• License:
• Abstract: Despite remarkable advancements in recent voice conversion (VC) systems, enhancing speaker similarity in zero-shot scenarios remains challenging. This challenge arises from the difficulty of generalizing and adapting speaker characteristics in speech within zero-shot environments, which is further complicated by mismatch between the training and inference processes. To address these challenges, we propose VoicePrompter, a robust zero-shot VC model that leverages in-context learning with voice prompts. VoicePrompter is composed of (1) a factorization method that disentangles speech components and (2) a DiT-based conditional flow matching (CFM) decoder that conditions on these factorized features and voice prompts. Additionally, (3) latent mixup is used to enhance in-context learning by combining various speaker features. This approach improves speaker similarity and naturalness in zero-shot VC by applying mixup to latent representations. Experimental results demonstrate that VoicePrompter outperforms existing zero-shot VC systems in terms of speaker similarity, speech intelligibility, and audio quality. Our demo is available at \url{https://hayeong0.github.io/VoicePrompter-demo/}.

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