| Wavlm: Large-scale self-supervised pre-training for full stack speech processing S Chen, C Wang, Z Chen, Y Wu, S Liu, Z Chen, J Li, N Kanda, T Yoshioka, ... JSTSP, 2022 | 1828 | 2022 |
| Neural codec language models are zero-shot text to speech synthesizers S Chen, C Wang, Y Wu, Z Zhang, L Zhou, S Liu, Z Chen, Y Liu, H Wang, ... TASLP, 2025 | 622* | 2025 |
| Beats: Audio pre-training with acoustic tokenizers S Chen, Y Wu, C Wang, S Liu, D Tompkins, Z Chen, F Wei ICML 2023, 2023 | 278 | 2023 |
| Recall and learn: Fine-tuning deep pretrained language models with less forgetting S Chen, Y Hou, Y Cui, W Che, T Liu, X Yu EMNLP 2020, 2020 | 215 | 2020 |
| Speak foreign languages with your own voice: Cross-lingual neural codec language modeling Z Zhang, L Zhou, C Wang, S Chen, Y Wu, S Liu, Z Chen, Y Liu, H Wang, ... arXiv preprint, 2023 | 157 | 2023 |
| Continuous speech separation with conformer S Chen, Y Wu, Z Chen, J Wu, J Li, T Yoshioka, C Wang, S Liu, M Zhou ICASSP 2021, 2021 | 151 | 2021 |
| Large-scale self-supervised speech representation learning for automatic speaker verification Z Chen, S Chen, Y Wu, Y Qian, C Wang, S Liu, Y Qian, M Zeng ICASSP 2022, 2022 | 140 | 2022 |
| Unispeech-sat: Universal speech representation learning with speaker aware pre-training S Chen, Y Wu, C Wang, Z Chen, Z Chen, S Liu, J Wu, Y Qian, F Wei, J Li, ... ICASSP 2022, 2022 | 99 | 2022 |
| Microsoft speaker diarization system for the voxceleb speaker recognition challenge 2020 X Xiao, N Kanda, Z Chen, T Zhou, T Yoshioka, S Chen, Y Zhao, G Liu, ... ICASSP 2021, 2021 | 83 | 2021 |
| Movie Gen: A Cast of Media Foundation Models MG Team arXiv preprint, 2024 | 76* | 2024 |
| Speechx: Neural codec language model as a versatile speech transformer X Wang, M Thakker, Z Chen, N Kanda, SE Eskimez, S Chen, M Tang, ... TASLP, 2024 | 66 | 2024 |
| Speechlm: Enhanced speech pre-training with unpaired textual data Z Zhang, S Chen, L Zhou, Y Wu, S Ren, S Liu, Z Yao, X Gong, L Dai, J Li, ... TASLP, 2024 | 52 | 2024 |
| VALL-E 2: Neural Codec Language Models are Human Parity Zero-Shot Text to Speech Synthesizers S Chen, S Liu, L Zhou, Y Liu, X Tan, J Li, S Zhao, Y Qian, F Wei arXiv preprint, 2024 | 48 | 2024 |
| Why does self-supervised learning for speech recognition benefit speaker recognition? S Chen, Y Wu, C Wang, S Liu, Z Chen, P Wang, G Liu, J Li, J Wu, X Yu, ... Interspeech 2022, 2022 | 45 | 2022 |
| Wavllm: Towards robust and adaptive speech large language model S Hu, L Zhou, S Liu, S Chen, H Hao, J Pan, X Liu, J Li, S Sivasankaran, ... arXiv preprint, 2024 | 44 | 2024 |
| Mothernets: Rapid deep ensemble learning A Wasay, B Hentschel, Y Liao, S Chen, S Idreos MLSys, 2020 | 41 | 2020 |
| Improving self-supervised learning for speech recognition with intermediate layer supervision C Wang, Y Wu, S Chen, S Liu, J Li, Y Qian, Z Yang ICASSP 2022, 2022 | 31* | 2022 |
| Don’t shoot butterfly with rifles: Multi-channel continuous speech separation with early exit transformer S Chen, Y Wu, Z Chen, T Yoshioka, S Liu, J Li, X Yu ICASSP 2021, 2021 | 26 | 2021 |
| Supervision-guided codebooks for masked prediction in speech pre-training C Wang, Y Wang, Y Wu, S Chen, J Li, S Liu, F Wei Interspeech 2022, 2022 | 22 | 2022 |
| Autoregressive speech synthesis without vector quantization L Meng, L Zhou, S Liu, S Chen, B Han, S Hu, Y Liu, J Li, S Zhao, X Wu, ... arXiv preprint, 2024 | 20 | 2024 |
Shujie Liu (刘树杰）Microsoft Research Asia在 microsoft.com 的电子邮件经过验证