StuPASE: Towards Low-Hallucination Studio-Quality Generative Speech Enhancement
Achieving high perceptual quality without hallucination remains a challenge in generative speech enhancement (SE). A representative approach, PASE, is robust to hallucination but has limited perceptual quality under adverse conditions. We propose StuPASE, built upon PASE to achieve studio-level quality while retaining its low-hallucination property. First, we show that finetuning PASE with dry targets rather than targets containing simulated early reflections substantially improves dereverberation. Second, to address performance limitations under strong additive noise, we replace the GAN-based generative module in PASE with a flow-matching module, enabling studio-quality generation even u...
| Authors | Xiaobin Rong, Jun Gao, Zheng Wang et al. |
|---|---|
| Primary category | eess.AS |
| Published | 10 March 2026 |
| Last updated at source | 17 June 2026 |
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