13 citations · 44 across the 18 of their papers we have counts for
18 papers
Speaking from Coarse to Fine: Improving Neural Codec Language Model via Multi-Scale Speech Coding and Generation
Haohan Guo, Fenglong Xie, Dongchao Yang +2
The neural codec language model (CLM) has demonstrated remarkable performance in text-to-speech (TTS) synthesis. However, troubled by ``recency bias", CLM lacks sufficient attentio…
SoCodec: A Semantic-Ordered Multi-Stream Speech Codec for Efficient Language Model Based Text-to-Speech Synthesis
Haohan Guo, Fenglong Xie, Kun Xie +4
The long speech sequence has been troubling language models (LM) based TTS approaches in terms of modeling complexity and efficiency. This work proposes SoCodec, a semantic-ordered…
SimpleSpeech 2: Towards Simple and Efficient Text-to-Speech with Flow-based Scalar Latent Transformer Diffusion Models
Dongchao Yang, Rongjie Huang, Yuanyuan Wang +5
Scaling Text-to-speech (TTS) to large-scale datasets has been demonstrated as an effective method for improving the diversity and naturalness of synthesized speech. At the high lev…
UniAudio 1.5: Large Language Model-driven Audio Codec is A Few-shot Audio Task Learner
Dongchao Yang, Haohan Guo, Yuanyuan Wang +5
The Large Language models (LLMs) have demonstrated supreme capabilities in text understanding and generation, but cannot be directly applied to cross-modal tasks without fine-tunin…
SimpleSpeech: Towards Simple and Efficient Text-to-Speech with Scalar Latent Transformer Diffusion Models
Dongchao Yang, Dingdong Wang, Haohan Guo +3
In this study, we propose a simple and efficient Non-Autoregressive (NAR) text-to-speech (TTS) system based on diffusion, named SimpleSpeech. Its simpleness shows in three aspects:…
Single-Codec: Single-Codebook Speech Codec towards High-Performance Speech Generation
Hanzhao Li, Liumeng Xue, Haohan Guo +6
The multi-codebook speech codec enables the application of large language models (LLM) in TTS but bottlenecks efficiency and robustness due to multi-sequence prediction. To avoid t…