128 citations · 245 across the 19 of their papers we have counts for
22 papers
SyntaSpeech: Syntax-Aware Generative Adversarial Text-to-Speech
Zhenhui Ye, Zhou Zhao, Yi Ren +1
The recent progress in non-autoregressive text-to-speech (NAR-TTS) has made fast and high-quality speech synthesis possible. However, current NAR-TTS models usually use phoneme seq…
FastDiff: A Fast Conditional Diffusion Model for High-Quality Speech Synthesis
Rongjie Huang, Max W. Y. Lam, Jun Wang +4
Denoising diffusion probabilistic models (DDPMs) have recently achieved leading performances in many generative tasks. However, the inherited iterative sampling process costs hinde…
Attributable-Watermarking of Speech Generative Models
Yongbaek Cho, Changhoon Kim, Yezhou Yang +1
Generative models are now capable of synthesizing images, speeches, and videos that are hardly distinguishable from authentic contents. Such capabilities cause concerns such as mal…
Learning the Beauty in Songs: Neural Singing Voice Beautifier
Jinglin Liu, Chengxi Li, Yi Ren +2
We are interested in a novel task, singing voice beautifying (SVB). Given the singing voice of an amateur singer, SVB aims to improve the intonation and vocal tone of the voice, wh…
Revisiting Over-Smoothness in Text to Speech
Yi Ren, Xu Tan, Tao Qin +2
Non-autoregressive text to speech (NAR-TTS) models have attracted much attention from both academia and industry due to their fast generation speed. One limitation of NAR-TTS model…
ProsoSpeech: Enhancing Prosody With Quantized Vector Pre-training in Text-to-Speech
Yi Ren, Ming Lei, Zhiying Huang +4
Expressive text-to-speech (TTS) has become a hot research topic recently, mainly focusing on modeling prosody in speech. Prosody modeling has several challenges: 1) the extracted p…