Conan: A Chunkwise Online Network for Zero-Shot Adaptive Voice Conversion
This addresses real-time voice conversion for communications and entertainment, but it appears incremental as it builds on existing methods like Emformer and HiFiGAN.
The paper tackled the problem of zero-shot online voice conversion by introducing Conan, a model that preserves source content while matching reference voice timbre and styles, and it outperformed baseline models in subjective and objective metrics.
Zero-shot online voice conversion (VC) holds significant promise for real-time communications and entertainment. However, current VC models struggle to preserve semantic fidelity under real-time constraints, deliver natural-sounding conversions, and adapt effectively to unseen speaker characteristics. To address these challenges, we introduce Conan, a chunkwise online zero-shot voice conversion model that preserves the content of the source while matching the voice timbre and styles of reference speech. Conan comprises three core components: 1) a Stream Content Extractor that leverages Emformer for low-latency streaming content encoding; 2) an Adaptive Style Encoder that extracts fine-grained stylistic features from reference speech for enhanced style adaptation; 3) a Causal Shuffle Vocoder that implements a fully causal HiFiGAN using a pixel-shuffle mechanism. Experimental evaluations demonstrate that Conan outperforms baseline models in subjective and objective metrics. Audio samples can be found at https://aaronz345.github.io/ConanDemo.