3 papers
eess.AS2026
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…
eess.AS2025
Lightweight speech enhancement guided target speech extraction in noisy multi-speaker scenarios
Ziling Huang, Junnan Wu, Lichun Fan +4
Target speech extraction (TSE) has achieved strong performance in relatively simple conditions such as one-speaker-plus-noise and two-speaker mixtures, but its performance remains…
cs.SD2023
Enhanced Neural Beamformer with Spatial Information for Target Speech Extraction
Aoqi Guo, Junnan Wu, Peng Gao +4
Recently, deep learning-based beamforming algorithms have shown promising performance in target speech extraction tasks. However, most systems do not fully utilize spatial informat…