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Voice Conversion(VC) refers to changing the timbre of a speech whileretaining the discourse content. Recently, many works have focused ondisentangle-based learning techniques to separate the timbre and the linguisticcontent information from a speech signal. Once successful, voice conversionwill be feasible and straightforward. This paper proposed a novel one-shotvoice conversion framework based on vector quantization voice conversion (VQVC)and AutoVC, called AVQVC. A new training method is applied to VQVC to separatecontent and timbre information from speech more effectively. The result showsthat this approach has better performance than VQVC in separating content andtimbre to improve the sound quality of generated speech.

AVQVC: One-shot Voice Conversion by Vector Quantization with applying contrastive learning