5 papers
Reducing Linguistic Hallucination in LM-Based Speech Enhancement via Noise-Invariant Acoustic-Semantic Distillation
Zheng Wang, Xiaobin Rong, Hang Su +6
Language model (LM)-based speech enhancement (SE) can generate natural-sounding speech, but under severe noise it often suffers from unreliable conditioning, leading to perceptuall…
GAP-URGENet: A Generative-Predictive Fusion Framework for Universal Speech Enhancement
Xiaobin Rong, Yushi Wang, Zheng Wang +1
We introduce GAP-URGENet, a generative-predictive fusion framework developed for Track 1 of the ICASSP 2026 URGENT Challenge. The system integrates a generative branch, which perfo…
UniPASE: A Generative Model for Universal Speech Enhancement with High Fidelity and Low Hallucinations
Xiaobin Rong, Zheng Wang, Yushi Wang +2
Universal speech enhancement (USE) aims to restore speech signals from diverse distortions across multiple sampling rates. We propose UniPASE, an extension of the low-hallucination…
StuPASE: Towards Low-Hallucination Studio-Quality Generative Speech Enhancement
Xiaobin Rong, Jun Gao, Zheng Wang +3
Achieving high perceptual quality without hallucination remains a challenge in generative speech enhancement (SE). A representative approach, PASE, is robust to hallucination but h…
A Lightweight Hybrid Dual Channel Speech Enhancement System under Low-SNR Conditions
Zheng Wang, Xiaobin Rong, Yu Sun +3
Although deep learning based multi-channel speech enhancement has achieved significant advancements, its practical deployment is often limited by constrained computational resource…