[Paper 리뷰] Flowtron: An Autoregressive Flow-based Generative Network for Text-to-Speech Synthesis
Flowtron: An Autoregressive Flow-based Generative Network for Text-to-Speech SynthesisStyle transfer, speech variation을 향상하기 위해 autoregressive flow-based generative network를 활용할 수 있음FlowtronTraining data의 likelihood를 maximizing 하여 optimize 되고 simple, stable training을 지원Timbre, expressivity, accent를 modulate할 수 있는 latent space에 대한 invertible mapping을 학습논문 (ICLR 2021) : Paper Link1. Introduction최근..
[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] TSP-TTS: Text-based Style Predictor with Residual Vector Quantization for Expressive Text-to-Speech
TSP-TTS: Text-based Style Predictor with Residual Vector Quantization for Expressive Text-to-SpeechExpressive text-to-speech는 다양한 speech style, emotion이 반영된 음성을 합성하는 것을 목표로 함TSP-TTSText 자체에서 추출한 style representation을 기반으로 condition 된 expressive text-to-speech modelText-based style predictor를 위해 Residual Vector Quantization을 도입하고 mel-decoder에 Style-Text Alignment와 Style Hierarchical Layer Normali..
[Paper 리뷰] FastPitchFormant: Source-Filter based Decomposed Modeling for Speech Synthesis
FastPitchFormant: Source-Filter based Decomposed Modeling for Speech SynthesisText-to-Speech에서 large pitch-shift scale은 품질 저하와 speaker characteristic deformation을 일으킴FastPitchFormantSource-Filter theory를 기반으로 설계된 Feed-Forward Transformer modelText, acoustic feature를 개별적으로 modeling 하여 model이 두 feature 간의 relationship을 학습하는 것을 방지논문 (INTERSPEECH 2021) : Paper Link1. IntroductionText-to-Speech (TTS)..
[Paper 리뷰] DPP-TTS: Diversifying Prosodic Features of Speech via Determinantal Point Process
DPP-TTS: Diversifying Prosodic Features of Speech via Determinantal Point ProcessesText-to-Speech model은 다양한 prosody를 합성할 수 있어야 함- BUT, 기존 model은 prosody diversity를 향상하기 위해 scaled sampling temperature에 의존함- Sampling procedure는 single speech sample에 focus 하므로 sample 간 diversity가 neglect 됨DPP-TTSProsody diversifying module과 Determinantal Point Process에 기반한 text-to-speech model여러 sample 간의 perceptu..