Few-Shot Keyword Spotting With Prototypical Networks

• URL: http://arxiv.org/abs/2007.14463v1
• Date: Sat, 25 Jul 2020 20:17:56 GMT
• Title: Few-Shot Keyword Spotting With Prototypical Networks
• Authors: Archit Parnami, Minwoo Lee
• Abstract summary: keyword spotting has been widely used in many voice interfaces such as Amazon's Alexa and Google Home. We first formulate this problem as a few-shot keyword spotting and approach it using metric learning. We then propose a solution to the prototypical few-shot keyword spotting problem using temporal and dilated convolutions on networks.
• Score: 3.6930948691311016
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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