163 citations · 430 across the 29 of their papers we have counts for
29 papers
Investigating Group Relative Policy Optimization for Diffusion Transformer based Text-to-Audio Generation
Yi Gu, Yanqing Liu, Chen Yang +1
Text-to-audio (T2A) generation has advanced considerably in recent years, yet existing methods continue to face challenges in accurately rendering complex text prompts, particularl…
A Unified Neural Codec Language Model for Selective Editable Text to Speech Generation
Hanchen Pei, Shujie Liu, Yanqing Liu +5
Neural codec language models achieve impressive zero-shot Text-to-Speech (TTS) by fully imitating the acoustic characteristics of a short speech prompt, including timbre, prosody,…
Continuous Speech Tokens Makes LLMs Robust Multi-Modality Learners
Ze Yuan, Yanqing Liu, Shujie Liu +1
Recent advances in GPT-4o like multi-modality models have demonstrated remarkable progress for direct speech-to-speech conversation, with real-time speech interaction experience an…
Investigating Neural Audio Codecs for Speech Language Model-Based Speech Generation
Jiaqi Li, Dongmei Wang, Xiaofei Wang +13
Neural audio codec tokens serve as the fundamental building blocks for speech language model (SLM)-based speech generation. However, there is no systematic understanding on how the…
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…
Boosting Diffusion Model for Spectrogram Up-sampling in Text-to-speech: An Empirical Study
Chong Zhang, Yanqing Liu, Yang Zheng +1
Scaling text-to-speech (TTS) with autoregressive language model (LM) to large-scale datasets by quantizing waveform into discrete speech tokens is making great progress to capture…
E2 TTS: Embarrassingly Easy Fully Non-Autoregressive Zero-Shot TTS
Sefik Emre Eskimez, Xiaofei Wang, Manthan Thakker +10
This paper introduces Embarrassingly Easy Text-to-Speech (E2 TTS), a fully non-autoregressive zero-shot text-to-speech system that offers human-level naturalness and state-of-the-a…
NaturalSpeech 3: Zero-Shot Speech Synthesis with Factorized Codec and Diffusion Models
Zeqian Ju, Yuancheng Wang, Kai Shen +16
While recent large-scale text-to-speech (TTS) models have achieved significant progress, they still fall short in speech quality, similarity, and prosody. Considering speech intric…
Making Flow-Matching-Based Zero-Shot Text-to-Speech Laugh as You Like
Naoyuki Kanda, Xiaofei Wang, Sefik Emre Eskimez +12
Laughter is one of the most expressive and natural aspects of human speech, conveying emotions, social cues, and humor. However, most text-to-speech (TTS) systems lack the ability…
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…
FoundationTTS: Text-to-Speech for ASR Customization with Generative Language Model
Ruiqing Xue, Yanqing Liu, Lei He +4
Neural text-to-speech (TTS) generally consists of cascaded architecture with separately optimized acoustic model and vocoder, or end-to-end architecture with continuous mel-spectro…
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…
Improving Contextual Spelling Correction by External Acoustics Attention and Semantic Aware Data Augmentation
Xiaoqiang Wang, Yanqing Liu, Jinyu Li +1
We previously proposed contextual spelling correction (CSC) to correct the output of end-to-end (E2E) automatic speech recognition (ASR) models with contextual information such as…
DREAM: Efficient Dataset Distillation by Representative Matching
Yanqing Liu, Jianyang Gu, Kai Wang +3
Dataset distillation aims to synthesize small datasets with little information loss from original large-scale ones for reducing storage and training costs. Recent state-of-the-art…
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…
DelightfulTTS 2: End-to-End Speech Synthesis with Adversarial Vector-Quantized Auto-Encoders
Yanqing Liu, Ruiqing Xue, Lei He +2
Current text to speech (TTS) systems usually leverage a cascaded acoustic model and vocoder pipeline with mel-spectrograms as the intermediate representations, which suffer from tw…
RetrieverTTS: Modeling Decomposed Factors for Text-Based Speech Insertion
Dacheng Yin, Chuanxin Tang, Yanqing Liu +6
This paper proposes a new "decompose-and-edit" paradigm for the text-based speech insertion task that facilitates arbitrary-length speech insertion and even full sentence generatio…
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…
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…
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…
DelightfulTTS: The Microsoft Speech Synthesis System for Blizzard Challenge 2021
Yanqing Liu, Zhihang Xu, Gang Wang +6
This paper describes the Microsoft end-to-end neural text to speech (TTS) system: DelightfulTTS for Blizzard Challenge 2021. The goal of this challenge is to synthesize natural and…