3 papers
cs.SD2026
One Model, Many Latencies: Universal Speech Enhancement for Diverse Real-Time Applications
Szu-Wei Fu, Rong Chao, Xuesong Yang +4
Different real-time speech applications impose distinct latency budgets, often requiring separately trained enhancement models for each scenario. In this paper, we propose a one-fo…
cs.SD2026
Rethinking Training Targets, Architectures and Data Quality for Universal Speech Enhancement
Szu-Wei Fu, Rong Chao, Xuesong Yang +6
Universal Speech Enhancement (USE) aims to restore speech quality under diverse degradation conditions while preserving signal fidelity. Despite recent progress, key challenges in…
eess.AS2026
Tracking Listener Attention: Gaze-Guided Audio-Visual Speech Enhancement Framework
Hsiang-Cheng Yang, You-Jin Li, Rong Chao +3
This paper presents a Gaze-Guided Audio-Visual Speech Enhancement (GG-AVSE) framework to address the cocktail party problem. A major challenge in conventional AVSE is identifying t…