515 citations · 1.2k across the 46 of their papers we have counts for
10 papers · 1 filter
ContextSpeech: Expressive and Efficient Text-to-Speech for Paragraph Reading
Yujia Xiao, Shaofei Zhang, Xi Wang +5
While state-of-the-art Text-to-Speech systems can generate natural speech of very high quality at sentence level, they still meet great challenges in speech generation for paragrap…
Speak Foreign Languages with Your Own Voice: Cross-Lingual Neural Codec Language Modeling
Ziqiang Zhang, Long Zhou, Chengyi Wang +10
We propose a cross-lingual neural codec language model, VALL-E X, for cross-lingual speech synthesis. Specifically, we extend VALL-E and train a multi-lingual conditional codec lan…
Neural Codec Language Models are Zero-Shot Text to Speech Synthesizers
Chengyi Wang, Sanyuan Chen, Yu Wu +10
We introduce a language modeling approach for text to speech synthesis (TTS). Specifically, we train a neural codec language model (called Vall-E) using discrete codes derived from…
VideoDubber: Machine Translation with Speech-Aware Length Control for Video Dubbing
Yihan Wu, Junliang Guo, Xu Tan +7
Video dubbing aims to translate the original speech in a film or television program into the speech in a target language, which can be achieved with a cascaded system consisting of…
Towards Contextual Spelling Correction for Customization of End-to-end Speech Recognition Systems
Xiaoqiang Wang, Yanqing Liu, Jinyu Li +3
Contextual biasing is an important and challenging task for end-to-end automatic speech recognition (ASR) systems, which aims to achieve better recognition performance by biasing t…
A Light-weight contextual spelling correction model for customizing transducer-based speech recognition systems
Xiaoqiang Wang, Yanqing Liu, Sheng Zhao +1
It's challenging to customize transducer-based automatic speech recognition (ASR) system with context information which is dynamic and unavailable during model training. In this wo…
A Study of Non-autoregressive Model for Sequence Generation
Yi Ren, Jinglin Liu, Xu Tan +3
Non-autoregressive (NAR) models generate all the tokens of a sequence in parallel, resulting in faster generation speed compared to their autoregressive (AR) counterparts but at th…
Semantic Mask for Transformer based End-to-End Speech Recognition
Chengyi Wang, Yu Wu, Yujiao Du +7
Attention-based encoder-decoder model has achieved impressive results for both automatic speech recognition (ASR) and text-to-speech (TTS) tasks. This approach takes advantage of t…
Token-Level Ensemble Distillation for Grapheme-to-Phoneme Conversion
Hao Sun, Xu Tan, Jun-Wei Gan +4
Grapheme-to-phoneme (G2P) conversion is an important task in automatic speech recognition and text-to-speech systems. Recently, G2P conversion is viewed as a sequence to sequence t…
Neural Speech Synthesis with Transformer Network
Naihan Li, Shujie Liu, Yanqing Liu +3
Although end-to-end neural text-to-speech (TTS) methods (such as Tacotron2) are proposed and achieve state-of-the-art performance, they still suffer from two problems: 1) low effic…