20 citations · 33 across the 18 of their papers we have counts for
19 papers
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,…
Next Tokens Denoising for Speech Synthesis
Yanqing Liu, Ruiqing Xue, Chong Zhang +7
While diffusion and autoregressive (AR) models have significantly advanced generative modeling, they each present distinct limitations. AR models, which rely on causal attention, c…
Zero-Shot Streaming Text to Speech Synthesis with Transducer and Auto-Regressive Modeling
Haiyang Sun, Shujie Hu, Shujie Liu +8
Zero-shot streaming text-to-speech is an important research topic in human-computer interaction. Existing methods primarily use a lookahead mechanism, relying on future text to ach…
CoVoMix2: Advancing Zero-Shot Dialogue Generation with Fully Non-Autoregressive Flow Matching
Leying Zhang, Yao Qian, Xiaofei Wang +8
Generating natural-sounding, multi-speaker dialogue is crucial for applications such as podcast creation, virtual agents, and multimedia content generation. However, existing syste…
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…
Laugh Now Cry Later: Controlling Time-Varying Emotional States of Flow-Matching-Based Zero-Shot Text-to-Speech
Haibin Wu, Xiaofei Wang, Sefik Emre Eskimez +8
People change their tones of voice, often accompanied by nonverbal vocalizations (NVs) such as laughter and cries, to convey rich emotions. However, most text-to-speech (TTS) syste…
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…
An Investigation of Noise Robustness for Flow-Matching-Based Zero-Shot TTS
Xiaofei Wang, Sefik Emre Eskimez, Manthan Thakker +8
Recently, zero-shot text-to-speech (TTS) systems, capable of synthesizing any speaker's voice from a short audio prompt, have made rapid advancements. However, the quality of the g…
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…
Total-Duration-Aware Duration Modeling for Text-to-Speech Systems
Sefik Emre Eskimez, Xiaofei Wang, Manthan Thakker +9
Accurate control of the total duration of generated speech by adjusting the speech rate is crucial for various text-to-speech (TTS) applications. However, the impact of adjusting t…
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…
RALL-E: Robust Codec Language Modeling with Chain-of-Thought Prompting for Text-to-Speech Synthesis
Detai Xin, Xu Tan, Kai Shen +8
We present RALL-E, a robust language modeling method for text-to-speech (TTS) synthesis. While previous work based on large language models (LLMs) shows impressive performance on z…
TransVIP: Speech to Speech Translation System with Voice and Isochrony Preservation
Chenyang Le, Yao Qian, Dongmei Wang +8
There is a rising interest and trend in research towards directly translating speech from one language to another, known as end-to-end speech-to-speech translation. However, most e…
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…
CoVoMix: Advancing Zero-Shot Speech Generation for Human-like Multi-talker Conversations
Leying Zhang, Yao Qian, Long Zhou +9
Recent advancements in zero-shot text-to-speech (TTS) modeling have led to significant strides in generating high-fidelity and diverse speech. However, dialogue generation, along w…
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…