  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.
