Parallel Time-Band Mixing with Learned Observation-Adding for Robust ASR Front-Ends
Read the original on arXiv AI →The paper introduces a sequence‑parallel band‑split enhancement front‑end called Parallel Time‑Band Mixer (PTBM) that removes recurrent unrolling within blocks. PTBM combines intra‑band temporal mixing with per‑frame cross‑band attention in a fully parallel architecture, while a learned Observation‑Adding (LOA) module suppresses ASR‑sensitive artifacts without development‑set tuning. Experiments on DNS Challenge and CHiME‑4 using frozen Whisper back‑ends show that this lightweight front‑end (0.96 M parameters, 0.58 GMAC/s) consistently lowers word error rate compared to recurrent band‑split baselines.
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