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
MDM-ASR: Bridging Accuracy and Efficiency in ASR with Diffusion-Based Non-Autoregressive Decoding
Hao Yen, Pin-Jui Ku, Ante JukiÄ +1
In sequence-to-sequence Transformer ASR, autoregressive (AR) models achieve strong accuracy but suffer from slow decoding, while non-autoregressive (NAR) models enable parallel dec…