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20182026
most citedNeural Codec Language Models are Zero-Shot Text to Speech Synthesizers

163 citations · 430 across the 29 of their papers we have counts for

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Showing 2024Show all

9 papers · 1 filter

cs.SD2024

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…

cs.SD2024

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…

cs.CL2024

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…

cs.CL2024★ 8 cited

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…

cs.CL2024★ 1 cited

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…

eess.AS2024

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…

eess.AS2024

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…

eess.AS2024★ 20 cited

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…

eess.AS2024★ 1 cited

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…