PASE is a state-of-the-art generative speech enhancement model trained to remove noise and reverberation while preserving linguistic content and speaker identity. It operates on 16 kHz mono audio.
Model Details
Model Description
High-level system design
PASE contains two main components:
Denoising WavLM (DeWavLM)
Fine‑tuned from WavLM‑Large using denoising representation distillation (DRD).
Performs robust noise supression while effectively mitigating linguistic hallucinations by leveraging the phonological prior from self-supervised WavLM.
Dual‑Stream Vocoder
Reconstructs audio using DeWavLM's dual-stream representations:
Phonetic representation: high-level linguistic structure
Acoustic representation: speaker identity and prosody
These source datasets were used to prepare training mixtures and train the released model. The model card and repository do not redistribute the underlying dataset contents; please refer to the original dataset pages and licenses below.
Dataset Attribution
DNS5 Challenge clean speech (LibriVox subset): clean-speech material prepared from LibriVox through the DNS Challenge. The LibriVox recordings used for this portion are public domain and were used as clean-speech training data for the released checkpoint.
LibriTTS: LibriTTS by Heiga Zen et al., licensed under CC BY 4.0. It was used as clean-speech training data for the released checkpoint.
VCTK Corpus: the VCTK dataset from the Centre for Speech Technology Research, University of Edinburgh, licensed under CC BY 4.0. It was used as clean-speech training data for the released checkpoint.
DNS5 Challenge noise resources: noise data prepared through the DNS Challenge and used to synthesize noisy training mixtures for the released checkpoint. For this release, the DNS5 noise resources draw on AudioSet material licensed under CC BY 4.0, selected Freesound files licensed under CC0 1.0, and DEMAND environmental recordings licensed under CC BY-SA 3.0.
OpenSLR26 and OpenSLR28: OpenSLR26 and OpenSLR28 room impulse response resources, both licensed under Apache 2.0, were used to add reverberation during training.
The performance of the released version compared to the paper's results:
Model
DNSMOS
UTMOS
SBS
LPS
SpkSim
WER (%)
Vocoder-L24 (paper)
3.23
3.40
0.94
0.97
0.65
2.86
Vocoder-L24 (released)
3.29
3.30
0.94
0.96
0.59
3.46
DeWavLM (paper)
3.26
3.42
0.88
0.93
0.57
7.62
DeWavLM (released)
3.31
3.39
0.88
0.93
0.52
7.25
PASE (paper)
3.12
3.09
0.90
0.93
0.80
7.49
PASE (released)
3.08
3.21
0.91
0.94
0.80
6.76
It can be seen that the released version achieves performance very close to that of the paper's results on our simulated test set.
Overall, PASE achieves:
Lowest WER among evaluated generative and discriminative baselines
Highest speaker similarity (SpkSim)
Strong perceptual quality with low hallucination rates
Consistent performance across noisy and reverberant conditions
Bias, Risks, and Limitations
Model trained primarily on English speech; performance may degrade for other languages.
Very strong noise or mismatched reverberation conditions can introduce artifacts.
Speaker characteristics are preserved but not guaranteed perfectly.
Recommendations
Evaluate outputs for your specific use case. Avoid deployments where misunderstanding enhanced speech could have safety or legal consequences.
Citation
If you use PASE in your research, please cite:
bibtex
1@article{PASE,
2 title={{PASE: Leveraging the Phonological Prior of WavLM for Low-Hallucination Generative Speech Enhancement}},
3 volume={40},
4 DOI={10.1609/aaai.v40i39.40562},
5 number={39},
6 journal={Proceedings of the AAAI Conference on Artificial Intelligence},
7 author={Rong, Xiaobin and Hu, Qinwen and Yesilbursa, Mansur and Wojcicki, Kamil and Lu, Jing},
8 year={2026},
9 month={Mar.},
10 pages={32826-32834}
11}
