arXiv Artificial Intelligence

StuPASE: Towards Low-Hallucination Studio-Quality Generative Speech Enhancement

StuPASE: Towards Low-Hallucination Studio-Quality Generative Speech Enhancement

Quick summary

arXiv:2603.09234v2 Announce Type: cross Abstract: 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 und

Key takeaways

  • arXiv:2603.09234v2 Announce Type: cross Abstract: 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.

Why it matters

“StuPASE: Towards Low-Hallucination Studio-Quality Generative Speech Enhancement” illustrates how changes in the AI ecosystem can affect products, workflows and user expectations together. Its lasting significance depends on measurable adoption, cost and safety outcomes.

Kaynak sitede devamını oku: arXiv Artificial Intelligence ↗