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20192026
most citedNaturalSpeech 2: Latent Diffusion Models are Natural and Zero-Shot Speech and Singing Synthesizers

38 citations · 206 across the 28 of their papers we have counts for

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12 papers · 1 filter

eess.AS2025★ 2 cited

Kimi-Audio Technical Report

KimiTeam, Ding Ding, Zeqian Ju +37

We present Kimi-Audio, an open-source audio foundation model that excels in audio understanding, generation, and conversation. We detail the practices in building Kimi-Audio, inclu…

eess.AS2025

MoonCast: High-Quality Zero-Shot Podcast Generation

Zeqian Ju, Dongchao Yang, Jianwei Yu +7

Recent advances in text-to-speech synthesis have achieved notable success in generating high-quality short utterances for individual speakers. However, these systems still face cha…

eess.AS2024★ 7 cited

Qwen2-Audio Technical Report

Yunfei Chu, Jin Xu, Qian Yang +9

We introduce the latest progress of Qwen-Audio, a large-scale audio-language model called Qwen2-Audio, which is capable of accepting various audio signal inputs and performing audi…

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

AIR-Bench: Benchmarking Large Audio-Language Models via Generative Comprehension

Qian Yang, Jin Xu, Wenrui Liu +8

Recently, instruction-following audio-language models have received broad attention for human-audio interaction. However, the absence of benchmarks capable of evaluating audio-cent…

eess.AS2023

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…

eess.AS2023★ 38 cited

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…

eess.AS2022★ 5 cited

ResGrad: Residual Denoising Diffusion Probabilistic Models for Text to Speech

Zehua Chen, Yihan Wu, Yichong Leng +9

Denoising Diffusion Probabilistic Models (DDPMs) are emerging in text-to-speech (TTS) synthesis because of their strong capability of generating high-fidelity samples. However, the…

eess.AS2022★ 2 cited

PromptTTS: Controllable Text-to-Speech with Text Descriptions

Zhifang Guo, Yichong Leng, Yihan Wu +2

Using a text description as prompt to guide the generation of text or images (e.g., GPT-3 or DALLE-2) has drawn wide attention recently. Beyond text and image generation, in this w…

eess.AS2022★ 35 cited

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…

eess.AS2022★ 29 cited

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…

eess.AS2021

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…