[Paper 리뷰] PURE Codec: Progressive Unfolding of Residual Entropy for Speech Codec Learning
PURE Codec: Progressive Unfolding of Residual Entropy for Speech Codec LearningNeural speech codec은 Residual Vector Quantization으로 인한 reconstruction의 한계가 있음PURE CodecPre-trained speech enhancement model을 활용하여 multi-stage quantization을 guidingFirst stage에서는 low-entropy, denoised speech embedding을 reconstruct 하고 second stage에서는 residual high-entropy component를 encode논문 (ASRU 2025) : Paper Link1. I..
[Paper 리뷰] DiffStyleTTS: Diffusion-based Hierarchical Prosody Modeling for Text-to-Speech with Diverse and Controllable Styles
DiffStyleTTS: Diffusion-based Hierarchical Prosody Modeling for Text-to-Speech with Diverse and Controllable StylesRich, flexible prosodic variation을 위해서는 text-to-prosody의 one-to-many mapping 문제를 해결해야 함DiffStyleTTSConditional diffusion module과 classifier-free guidance를 활용Speech prosodic feature를 hierarchically modeling 하고 다양한 prosodic style을 control논문 (Coling 2025) : Paper Link1. IntroductionTex..
[Paper 리뷰] Language-Codec: Bridging Discrete Codec Representations and Speech Language Models
Language-Codec: Bridging Discrete Codec Representations and Speech Language ModelsDiscrete acoustic codec은 speech language model에서 intermediate representation으로 사용됨Language-CodecMasked Channel Residual Vector Quantization을 도입하여 initial codebook의 excessive information 문제를 해결추가적으로 Fourier transform structure, attention block, refined discriminator를 적용논문 (ACL 2025) : Paper Link1. IntroductionVALL-E..
[Paper 리뷰] SimpleSpeech2: Towards Simple and Efficient Text-to-Speech with Flow-based Scalar Latent Transformer Diffusion Models
SimpleSpeech2: Towards Simple and Efficient Text-to-Speech with Flow-based Scalar Latent Transformer Diffusion ModelsNon-autoregressive Text-to-Speech model은 duration alignment로 인한 complexity가 있음SimpleSpeech2Autoregressive, Non-autoregressive approach를 combine 하여 straightforward model을 구성Simplified data preparation, fast inference, stable generation을 지원논문 (TASLP 2025) : Paper Link1. Introduction..
[Paper 리뷰] Efficient Speech Language Modeling via Energy Distance in Continuous Latent Space
Efficient Speech Language Modeling via Energy Distance in Continuous Latent SpaceSpeech language model은 discretization으로 인한 한계가 있음SLEDSpeech waveform을 continuous latent representation의 sequence로 encodingEnergy distance objective를 사용하여 autoregressive modeling을 수행논문 (NeurIPS 2025) : Paper Link1. IntroductionSpeech audio는 integer/floating-point range내의 value를 가지는 lengthy sampling point sequence로 re..
[Paper 리뷰] Koel-TTS: Enhancing LLM based Speech Generation with Preference Alignment and Classifier Free Guidance
Koel-TTS: Enhancing LLM based Speech Generation with Preference Alignment and Classifier Free GuidanceAutoregressive speech token generation model은 hallucination과 undesired vocalization의 문제가 있음Koel-TTSPreference alignment와 Classifier Free Guidance를 활용하여 Language Model의 contextual adherence를 향상특히 speech recognition model에서 derive 된 automatic metric을 사용하여 model output을 rank 하고 conditional, uncondi..