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

38 citations · 58 across the 5 of their papers we have counts for

collaborators

5 papers

cs.CL2024

Sentence-Level or Token-Level? A Comprehensive Study on Knowledge Distillation

Jingxuan Wei, Linzhuang Sun, Yichong Leng +3

Knowledge distillation, transferring knowledge from a teacher model to a student model, has emerged as a powerful technique in neural machine translation for compressing models or…

eess.AS202420 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.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…

cs.CL2023

Extract and Attend: Improving Entity Translation in Neural Machine Translation

Zixin Zeng, Rui Wang, Yichong Leng +4

While Neural Machine Translation(NMT) has achieved great progress in recent years, it still suffers from inaccurate translation of entities (e.g., person/organization name, locatio…

eess.AS202338 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…