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Parallel Time-Band Mixing with Learned Observation-Adding for Robust ASR Front-Ends
Speech enhancement is often used as a front-end for robust ASR, yet recurrent temporal and cross-band modules introduce sequential dependencies that reduce parallel efficiency. In this paper, we present a sequence-parallel band-split enhancement front-end built on a Parallel Time-Band Mixer (PTBM) block that eliminates within-block recurrent unrolling. PTBM integrates intra-band temporal mixing and per-frame cross-band attention within a unified parallel architecture, enabling efficient contextual modeling across both time and frequency dimensions. The system retains the mask-plus-residual rec
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Evidence & attribution
- arXiv · AI, language, vision and robotics · 2026-08-31T06:43:17.000Z
First collected: 2026-09-21T07:22:03.933Z. This is not the publication date.