3 papers
eess.AS2026
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…
eess.AS2026
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…
eess.AS2025
TS-URGENet: A Three-stage Universal Robust and Generalizable Speech Enhancement Network
Xiaobin Rong, Dahan Wang, Qinwen Hu +3
Universal speech enhancement aims to handle input speech with different distortions and input formats. To tackle this challenge, we present TS-URGENet, a Three-Stage Universal, Rob…