Abstract
Keyword spotting (KWS) is in great demand in smart devices in the era of Internet of Things. Albeit recent progresses, the performance of KWS, measured in false alarms and false rejects, may still degrade significantly under the far field and noisy conditions. In this paper, we propose integrating multiple beamformed signals and a microphone signal as input to an end-to-end KWS model and leveraging the attention mechanism to dynamically tune the model’s attention to the reliable input sources. We demonstrate, on our large simulated and recorded noisy and far-field evaluation sets, that our proposed approach significantly improves the KWS performance and reduces the computation cost against the baseline KWS systems.
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