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Few-Shot Keyword Spotting With Prototypical Networks

2020-07-25 20:17:56
Archit Parnami, Minwoo Lee
arXiv_SD
           
Abstract

Recognizing a particular command or a keyword, keyword spotting has been widely used in many voice interfaces such as Amazon's Alexa and Google Home. In order to recognize a set of keywords, most of the recent deep learning based approaches use a neural network trained with a large number of samples to identify certain pre-defined keywords. This restricts the system from recognizing new, user-defined keywords. Therefore, we first formulate this problem as a few-shot keyword spotting and approach it using metric learning. To enable this research, we also synthesize and publish a Few-shot Google Speech Commands dataset. We then propose a solution to the few-shot keyword spotting problem using temporal and dilated convolutions on prototypical networks. Our comparative experimental results demonstrate keyword spotting of new keywords using just a small number of samples.

Abstract (translated)

URL

https://arxiv.org/abs/2007.14463

PDF

https://arxiv.org/pdf/2007.14463.pdf

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