Papers by Zhisheng Zheng
emotion2vec: Self-Supervised Pre-Training for Speech Emotion Representation (2024.findings-acl)
Copied to clipboard
| Challenge: | Existing models for speech emotion recognition are not suitable for emotional tasks. |
| Approach: | They propose a universal speech emotion representation model that is pre-trained on open-source emotion data. |
| Outcome: | euphoria2vec outperforms state-of-the-art models and emotion specialist models . it shows consistent improvements among 10 different languages of speech emotion recognition datasets . |
VoiceStar: Robust Zero-Shot Autoregressive TTS with Duration Control and Extrapolation (2026.findings-acl)
Copied to clipboard
| Challenge: | Neural codec language models (NCLMs) lack fine-grained controllability and inability to extrapolate to sequence lengths much longer than those seen during training. |
| Approach: | They propose a novel autoregressive encoder-decoder neural codec language model that can be trained with a Continuation-Prompt Mixed training system. |
| Outcome: | The proposed model outperforms or is on par with current state-of-the-art models on short-form benchmarks such as LibriSpeech and Seed-TTS in terms of intelligibility and naturalness. |
Scaling Rich Style-Prompted Text-to-Speech Datasets (2025.emnlp-main)
Copied to clipboard
| Challenge: | Existing datasets that only cover basic tags are limited in their scale or coverage of style tags. |
| Approach: | They propose a large-scale dataset that annotates speech utterances with rich style captions. |
| Outcome: | The proposed dataset scales speech utterances with rich style captions for the first time. |
VoiceCraft-X: Unifying Multilingual, Voice-Cloning Speech Synthesis and Speech Editing (2025.emnlp-main)
Copied to clipboard
Zhisheng Zheng, Puyuan Peng, Anuj Diwan, Cong Phuoc Huynh, Xiaohang Sun, Zhu Liu, Vimal Bhat, David Harwath
| Challenge: | Autoregressive language model for multilingual speech editing and zero-shot text-to-speech synthesis is available in 11 languages. |
| Approach: | They introduce an autoregressive neural codec language model which unifies multilingual speech editing and zero-shot text-to-speech synthesis across 11 languages. |
| Outcome: | The model generates high-quality, natural-sounding speech, even with limited per-language data . it shows robust performance in diverse linguistic settings, even in limited per language data compared to other models . |