[Paper 리뷰] Expressive-VC: Highly Expressive Voice Conversion with Attention Fusion of Bottleneck and Perturbation Features
Expressive-VC: Highly Expressive Voice Conversion with Attention Fusion of Bottleneck and Perturbation FeaturesVoice conversion은 speaker similarity, intelligibility, expressiveness 측면에서 한계가 있음Expressive-VCNeural bottleneck feature approach와 information perturbation approach를 결합한 end-to-end voice conversion modelBottleneck feature encoder와 perturbe wav encoder를 사용하여 linguistic, para-linguistic fe..
[Paper 리뷰] DualVC3: Leveraging Language Model Generated Pseudo Context for End-to-End Low Latency Streaming Voice Conversion
DualVC3: Leveraging Language Model Generated Pseudo Context for End-to-End Low Latency Streaming Voice Conversion최근의 DualVC2는 180ms의 latency로 streaming voice conversion이 가능함- BUT, recognition-synthesis framework로 인해 end-to-end optimization이 어렵고 short chunk를 사용하는 경우 instability가 증가함DualVC3Speaker-independent semantic token을 사용하여 content encoder training을 guideLanguage model을 content encoder outpu..
[Paper 리뷰] Diff-HierVC: Diffusion-based Hierarchical Voice Conversion with Robust Pitch Generation and Masked Prior for Zero-Shot Speaker Adaptation
Diff-HierVC: Diffusion-based Hierarchical Voice Conversion with Robust Pitch Generation and Masked Prior for Zero-Shot Speaker AdaptationVoice Conversion은 여전히 inaccurate pitch와 low speaker adaptation 문제를 가지고 있음Diff-HierVC2가지 diffusion model을 기반으로 하는 hierarchical voice conversion model- Target voice style로 $F_{0}$를 효과적으로 생성할 수 있는 DiffPitch를 도입하고,- 이후 생성된 $F_{0}$를 DiffVoice에 전달하여 target voice styl..
[Paper 리뷰] StreamVoice: Streamable Context-Aware Language Modeling for Real-time Zero-Shot Voice Conversion
StreamVoice: Streamable Context-Aware Language Modeling for Real-time Zero-Shot Voice ConversionLanguage Model을 활용하여 zero-shot voice conversion 성능을 향상할 수 있음- BUT, 기존 방식은 offline conversion으로 인해 complete source speech 만을 요구하므로 real-time application에서 활용하기 어려움StreamVoiceStreaming capability를 위해 temporal independent acoustic predictor를 포함한 fully causal context-aware Language Model을 도입- 이를 통해 comple..
[Paper 리뷰] DDDM-VC: Decoupled Denoising Diffusion Models with Disentangled Representation and Prior Mixup for Verified Robust Voice Conversion
DDDM-VC: Decoupled Denoising Diffusion Models with Disentangled Representation and Prior Mixup for Verified Robust Voice ConversionDiffusion-based model은 data distribution에 많은 attribute가 존재하고 generation process에서 model parameter sharing에 대한 한계로 인해 각 attribute에 대한 specific style control이 어려움DDDM-VCDecoupled Denoising Diffusion Model을 도입하여 각 attribute에 대한 style transfer를 지원- 특히 voice conversion ta..
[Paper 리뷰] DiffVC: Diffusion-based Voice Conversion with Fast Maximum Likelihood Sampling Scheme
DiffVC: Diffusion-based Voice Conversion with Fast Maximum Likelihood Sampling SchemeOne-shot many-to-many voice conversion은 source/target speaker가 모두 training dataset에 속하지 않은 경우에 대해서 single reference utterance의 target voice를 copy 하는 것을 목표로 함DiffVCDiffusion probabilistic modeling을 기반으로 scalable one-shot voice conversion을 수행추가적으로 diffusion model을 가속할 수 있는 Stochastic Differential Equation solver를 ..