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arXiv · 1811.00348

Sequence-to-sequence Models for Small-Footprint Keyword Spotting

Haitong Zhang·Junbo Zhang·Yujun Wang
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Abstract

In this paper, we propose a sequence-to-sequence model for keyword spotting (KWS). Compared with other end-to-end architectures for KWS, our model simplifies the pipelines of production-quality KWS system and satisfies the requirement of high accuracy, low-latency, and small-footprint. We also evaluate the performances of different encoder architectures, which include LSTM and GRU. Experiments on the real-world wake-up data show that our approach outperforms the recently proposed attention-based end-to-end model. Specifically speaking, with 73K parameters, our sequence-to-sequence model achieves $\sim$3.05\% false rejection rate (FRR) at 0.1 false alarm (FA) per hour.

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BibTeXRIS

Haitong Zhang, Junbo Zhang, Yujun Wang. 2018-11-01. Sequence-to-sequence Models for Small-Footprint Keyword Spotting. https://arxiv.org/abs/1811.00348

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