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2018

Attention-based End-to-End Models for Small-Footprint Keyword Spotting

Shan, Changhao, Zhang, Junbo, Wang, Yujun et al.

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In this paper, we propose an attention-based end-to-end neural approach for small-footprint keyword spotting (KWS), which aims to simplify the pipelines of building a production-quality KWS system.

Our model consists of an encoder and an attention mechanism.
The encoder transforms the input signal into a high level representation using RNNs.
Then the attention mechanism weights the encoder features and generates a fixed-length vector.

Attention-based End-to-End Models for Small-Footprint Keyword Spotting · Around

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Then

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