# Lightweight Front-end Enhancement for Robust ASR via Frame Resampling and Sub-Band Pruning

arXiv: https://arxiv.org/abs/2509.21833 · HTML: https://mlsanity.com/paper/2509.21833/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 26 Sep 2025.

## Built on by (most cited first, 1 of 1)

- [Parallel Time-Band Mixing with Learned Observation-Adding for Robust ASR Front-Ends](https://mlsanity.com/paper/2608.30326/) (2026)

## Abstract (arXiv)

Recent advancements in automatic speech recognition (ASR) have achieved notable progress, whereas robustness in noisy environments remains challenging. While speech enhancement (SE) front-ends are widely used to mitigate noise as a preprocessing step for ASR, they often introduce computational non-negligible overhead. This paper proposes optimizations to reduce SE computational costs without compromising ASR performance. Our approach integrates layer-wise frame resampling and progressive sub-band pruning. Frame resampling downsamples inputs within layers, utilizing residual connections to mitigate information loss. Simultaneously, sub-band pruning progressively excludes less informative frequency bands, further reducing computational demands. Extensive experiments on synthetic and real-world noisy datasets demonstrate that our system reduces SE computational overhead over 66 compared to the standard BSRNN, while maintaining strong ASR performance.