84 citations · 233 across the 24 of their papers we have counts for
8 papers · 1 filter
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: An Audio Foundation Model Toward Universal Audio Generation
Dongchao Yang, Jinchuan Tian, Xu Tan +9
Large Language models (LLM) have demonstrated the capability to handle a variety of generative tasks. This paper presents the UniAudio system, which, unlike prior task-specific app…
The Singing Voice Conversion Challenge 2023
Wen-Chin Huang, Lester Phillip Violeta, Songxiang Liu +2
We present the latest iteration of the voice conversion challenge (VCC) series, a bi-annual scientific event aiming to compare and understand different voice conversion (VC) system…
HiFi-Codec: Group-residual Vector quantization for High Fidelity Audio Codec
Dongchao Yang, Songxiang Liu, Rongjie Huang +3
Audio codec models are widely used in audio communication as a crucial technique for compressing audio into discrete representations. Nowadays, audio codec models are increasingly…
Diverse and Expressive Speech Prosody Prediction with Denoising Diffusion Probabilistic Model
Xiang Li, Songxiang Liu, Max W. Y. Lam +3
Expressive human speech generally abounds with rich and flexible speech prosody variations. The speech prosody predictors in existing expressive speech synthesis methods mostly pro…
InstructTTS: Modelling Expressive TTS in Discrete Latent Space with Natural Language Style Prompt
Dongchao Yang, Songxiang Liu, Rongjie Huang +2
Expressive text-to-speech (TTS) aims to synthesize different speaking style speech according to human's demands. Nowadays, there are two common ways to control speaking styles: (1)…
NoreSpeech: Knowledge Distillation based Conditional Diffusion Model for Noise-robust Expressive TTS
Dongchao Yang, Songxiang Liu, Jianwei Yu +3
Expressive text-to-speech (TTS) can synthesize a new speaking style by imiating prosody and timbre from a reference audio, which faces the following challenges: (1) The highly dyna…
VARA-TTS: Non-Autoregressive Text-to-Speech Synthesis based on Very Deep VAE with Residual Attention
Peng Liu, Yuewen Cao, Songxiang Liu +4
This paper proposes VARA-TTS, a non-autoregressive (non-AR) text-to-speech (TTS) model using a very deep Variational Autoencoder (VDVAE) with Residual Attention mechanism, which re…