163 citations · 430 across the 29 of their papers we have counts for
8 papers · 1 filter
Autoregressive Speech Synthesis without Vector Quantization
Lingwei Meng, Long Zhou, Shujie Liu +9
We present MELLE, a novel continuous-valued token based language modeling approach for text-to-speech synthesis (TTS). MELLE autoregressively generates continuous mel-spectrogram f…
VALL-E 2: Neural Codec Language Models are Human Parity Zero-Shot Text to Speech Synthesizers
Sanyuan Chen, Shujie Liu, Long Zhou +6
This paper introduces VALL-E 2, the latest advancement in neural codec language models that marks a milestone in zero-shot text-to-speech synthesis (TTS), achieving human parity fo…
VALL-E R: Robust and Efficient Zero-Shot Text-to-Speech Synthesis via Monotonic Alignment
Bing Han, Long Zhou, Shujie Liu +7
With the help of discrete neural audio codecs, large language models (LLM) have increasingly been recognized as a promising methodology for zero-shot Text-to-Speech (TTS) synthesis…
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…
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…
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…