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arXiv · 1811.00350

End-to-end Models with auditory attention in Multi-channel Keyword Spotting

Haitong Zhang·Junbo Zhang·Yujun Wang
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Abstract

In this paper, we propose an attention-based end-to-end model for multi-channel keyword spotting (KWS), which is trained to optimize the KWS result directly. As a result, our model outperforms the baseline model with signal pre-processing techniques in both the clean and noisy testing data. We also found that multi-task learning results in a better performance when the training and testing data are similar. Transfer learning and multi-target spectral mapping can dramatically enhance the robustness to the noisy environment. At 0.1 false alarm (FA) per hour, the model with transfer learning and multi-target mapping gain an absolute 30% improvement in the wake-up rate in the noisy data with SNR about -20.

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Haitong Zhang, Junbo Zhang, Yujun Wang. 2018-11-01. End-to-end Models with auditory attention in Multi-channel Keyword Spotting. https://arxiv.org/abs/1811.00350

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