1.2k citations · 9.1k across the 205 of their papers we have counts for
13 papers · 1 filter
NaturalSpeech: End-to-End Text to Speech Synthesis with Human-Level Quality
Xu Tan, Jiawei Chen, Haohe Liu +11
Text to speech (TTS) has made rapid progress in both academia and industry in recent years. Some questions naturally arise that whether a TTS system can achieve human-level quality…
BinauralGrad: A Two-Stage Conditional Diffusion Probabilistic Model for Binaural Audio Synthesis
Yichong Leng, Zehua Chen, Junliang Guo +9
Binaural audio plays a significant role in constructing immersive augmented and virtual realities. As it is expensive to record binaural audio from the real world, synthesizing the…
AdaSpeech 4: Adaptive Text to Speech in Zero-Shot Scenarios
Yihan Wu, Xu Tan, Bohan Li +5
Adaptive text to speech (TTS) can synthesize new voices in zero-shot scenarios efficiently, by using a well-trained source TTS model without adapting it on the speech data of new s…
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…
A Survey on Neural Speech Synthesis
Xu Tan, Tao Qin, Frank Soong +1
Text to speech (TTS), or speech synthesis, which aims to synthesize intelligible and natural speech given text, is a hot research topic in speech, language, and machine learning co…
AdaSpeech: Adaptive Text to Speech for Custom Voice
Mingjian Chen, Xu Tan, Bohan Li +4
Custom voice, a specific text to speech (TTS) service in commercial speech platforms, aims to adapt a source TTS model to synthesize personal voice for a target speaker using few s…
DenoiSpeech: Denoising Text to Speech with Frame-Level Noise Modeling
Chen Zhang, Yi Ren, Xu Tan +5
While neural-based text to speech (TTS) models can synthesize natural and intelligible voice, they usually require high-quality speech data, which is costly to collect. In many sce…
HiFiSinger: Towards High-Fidelity Neural Singing Voice Synthesis
Jiawei Chen, Xu Tan, Jian Luan +2
High-fidelity singing voices usually require higher sampling rate (e.g., 48kHz) to convey expression and emotion. However, higher sampling rate causes the wider frequency band and…
LRSpeech: Extremely Low-Resource Speech Synthesis and Recognition
Jin Xu, Xu Tan, Yi Ren +4
Speech synthesis (text to speech, TTS) and recognition (automatic speech recognition, ASR) are important speech tasks, and require a large amount of text and speech pairs for model…
DeepSinger: Singing Voice Synthesis with Data Mined From the Web
Yi Ren, Xu Tan, Tao Qin +3
In this paper, we develop DeepSinger, a multi-lingual multi-singer singing voice synthesis (SVS) system, which is built from scratch using singing training data mined from music we…
UWSpeech: Speech to Speech Translation for Unwritten Languages
Chen Zhang, Xu Tan, Yi Ren +3
Existing speech to speech translation systems heavily rely on the text of target language: they usually translate source language either to target text and then synthesize target s…
FastSpeech 2: Fast and High-Quality End-to-End Text to Speech
Yi Ren, Chenxu Hu, Xu Tan +4
Non-autoregressive text to speech (TTS) models such as FastSpeech can synthesize speech significantly faster than previous autoregressive models with comparable quality. The traini…
Almost Unsupervised Text to Speech and Automatic Speech Recognition
Yi Ren, Xu Tan, Tao Qin +3
Text to speech (TTS) and automatic speech recognition (ASR) are two dual tasks in speech processing and both achieve impressive performance thanks to the recent advance in deep lea…