578 citations · 2.9k across the 69 of their papers we have counts for
19 papers · 1 filter
PromptTTS 2: Describing and Generating Voices with Text Prompt
Yichong Leng, Zhifang Guo, Kai Shen +12
Speech conveys more information than text, as the same word can be uttered in various voices to convey diverse information. Compared to traditional text-to-speech (TTS) methods rel…
NaturalSpeech 2: Latent Diffusion Models are Natural and Zero-Shot Speech and Singing Synthesizers
Kai Shen, Zeqian Ju, Xu Tan +6
Scaling text-to-speech (TTS) to large-scale, multi-speaker, and in-the-wild datasets is important to capture the diversity in human speech such as speaker identities, prosodies, an…
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…
Mixed-Phoneme BERT: Improving BERT with Mixed Phoneme and Sup-Phoneme Representations for Text to Speech
Guangyan Zhang, Kaitao Song, Xu Tan +8
Recently, leveraging BERT pre-training to improve the phoneme encoder in text to speech (TTS) has drawn increasing attention. However, the works apply pre-training with character-b…
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 study on the efficacy of model pre-training in developing neural text-to-speech system
Guangyan Zhang, Yichong Leng, Daxin Tan +5
In the development of neural text-to-speech systems, model pre-training with a large amount of non-target speakers' data is a common approach. However, in terms of ultimately achie…
PDAugment: Data Augmentation by Pitch and Duration Adjustments for Automatic Lyrics Transcription
Chen Zhang, Jiaxing Yu, LuChin Chang +4
Automatic lyrics transcription (ALT), which can be regarded as automatic speech recognition (ASR) on singing voice, is an interesting and practical topic in academia and industry.…
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…
MultiSpeech: Multi-Speaker Text to Speech with Transformer
Mingjian Chen, Xu Tan, Yi Ren +5
Transformer-based text to speech (TTS) model (e.g., Transformer TTS~\cite{li2019neural}, FastSpeech~\cite{ren2019fastspeech}) has shown the advantages of training and inference eff…
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…
XiaoiceSing: A High-Quality and Integrated Singing Voice Synthesis System
Peiling Lu, Jie Wu, Jian Luan +2
This paper presents XiaoiceSing, a high-quality singing voice synthesis system which employs an integrated network for spectrum, F0 and duration modeling. We follow the main archit…
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…