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Few-Shot Keyword Spotting With Prototypical Networks

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Document pages: 6 pages

Abstract: Recognizing a particular command or a keyword, keyword spotting has beenwidely used in many voice interfaces such as Amazon s Alexa and Google Home. Inorder to recognize a set of keywords, most of the recent deep learning basedapproaches use a neural network trained with a large number of samples toidentify certain pre-defined keywords. This restricts the system fromrecognizing new, user-defined keywords. Therefore, we first formulate thisproblem as a few-shot keyword spotting and approach it using metric learning.To enable this research, we also synthesize and publish a Few-shot GoogleSpeech Commands dataset. We then propose a solution to the few-shot keywordspotting problem using temporal and dilated convolutions on prototypicalnetworks. Our comparative experimental results demonstrate keyword spotting ofnew keywords using just a small number of samples.

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