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Voice conversion is an increasingly popular technology, and the growingnumber of real-time applications requires models with streaming conversioncapabilities. Unlike typical (non-streaming) voice conversion, which canleverage the entire utterance as full context, streaming voice conversion facessignificant challenges due to the missing future information, resulting indegraded intelligibility, speaker similarity, and sound quality. To addressthis challenge, we propose DualVC, a dual-mode neural voice conversion approachthat supports both streaming and non-streaming modes using jointly trainedseparate network parameters. Furthermore, we propose intra-model knowledgedistillation and hybrid predictive coding (HPC) to enhance the performance ofstreaming conversion. Additionally, we incorporate data augmentation to train anoise-robust autoregressive decoder, improving the model's performance onlong-form speech conversion. Experimental results demonstrate that the proposedmodel outperforms the baseline models in the context of streaming voiceconversion, while maintaining comparable performance to the non-streamingtopline system that leverages the complete context, albeit with a latency ofonly 252.8 ms.

DualVC: Dual-mode Voice Conversion using Intra-model Knowledge Distillation and Hybrid Predictive Coding