# End-to-End Multi-Look Keyword Spotting

arXiv: https://arxiv.org/abs/2005.10386 · HTML: https://mlsanity.com/paper/2005.10386/

## Key facts

- Cited by 4 papers in the map (data through 19 Oct 2026).
- First posted on arXiv 20 May 2020.

## Built on by (most cited first, 4 of 4)

- [Deep Spoken Keyword Spotting: An Overview](https://mlsanity.com/paper/2111.10592/) (2021)
- [The CUHK-TENCENT speaker diarization system for the ICASSP 2022 multi-channel multi-party meeting transcription challenge](https://mlsanity.com/paper/2202.01986/) (2022)
- [Multi-channel Speech Enhancement with 2-D Convolutional Time-frequency Domain Features and a Pre-trained Acoustic Model](https://mlsanity.com/paper/2107.11222/) (2021)
- [Does Single-channel Speech Enhancement Improve Keyword Spotting Accuracy? A Case Study](https://mlsanity.com/paper/2309.16060/) (2023)

## Abstract (arXiv)

The performance of keyword spotting (KWS), measured in false alarms and false rejects, degrades significantly under the far field and noisy conditions. In this paper, we propose a multi-look neural network modeling for speech enhancement which simultaneously steers to listen to multiple sampled look directions. The multi-look enhancement is then jointly trained with KWS to form an end-to-end KWS model which integrates the enhanced signals from multiple look directions and leverages an attention mechanism to dynamically tune the model's attention to the reliable sources. We demonstrate, on our large noisy and far-field evaluation sets, that the proposed approach significantly improves the KWS performance against the baseline KWS system and a recent beamformer based multi-beam KWS system.