Hierarchical Neural Network Architecture In Keyword Spotting

Yixiao Qu; Sihao Xue; Zhenyi Ying; Hang Zhou; Jue Sun

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arXiv · 1811.02320

Hierarchical Neural Network Architecture In Keyword Spotting

Yixiao Qu·Sihao Xue·Zhenyi Ying·Hang Zhou·Jue Sun

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Abstract

Keyword Spotting (KWS) provides the start signal of ASR problem, and thus it is essential to ensure a high recall rate. However, its real-time property requires low computation complexity. This contradiction inspires people to find a suitable model which is small enough to perform well in multi environments. To deal with this contradiction, we implement the Hierarchical Neural Network(HNN), which is proved to be effective in many speech recognition problems. HNN outperforms traditional DNN and CNN even though its model size and computation complexity are slightly less. Also, its simple topology structure makes easy to deploy on any device.

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BibTeX RIS

Yixiao Qu, Sihao Xue, Zhenyi Ying, Hang Zhou, Jue Sun. 2018-11-06. Hierarchical Neural Network Architecture In Keyword Spotting. https://arxiv.org/abs/1811.02320

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