Few-Shot Keyword Spotting With Prototypical Networks

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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• Prototypical Networks for Few-shot Learning
• Speech Commands: A Dataset for Limited-Vocabulary Speech Recognition
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Cited by in corpus (3)

• Proposal-based Few-shot Sound Event Detection for Speech and Environmental Sounds with Perceivers
• Self-Learning for Personalized Keyword Spotting on Ultra-Low-Power Audio Sensors
• Auto-KWS 2021 Challenge: Task, Datasets, and Baselines