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.

Stats

• citations22
• S2 citations—
• github stars0
• HF likes0
• heat score10.21
• arxiv keyparnami2020few

Related papers

• Few-shot Open-set Learning For On-device Customization Of Keyword Spotting Systems (2023)8.60
• Deep Residual Learning For Small-footprint Keyword Spotting (2017)16.21
• Predicting Detection Filters For Small Footprint Open-vocabulary Keyword Spotting (2019)9.92
• Speech Recognition: Keyword Spotting Through Image Recognition (2018)0.00
• Small-footprint Open-vocabulary Keyword Spotting With Quantized LSTM Networks (2020)0.00
• Neural Architecture Search For Keyword Spotting (2020)10.61
• Small-footprint Keyword Spotting With Graph Convolutional Network (2019)10.48
• Few Shot Speaker Recognition Using Deep Neural Networks (2019)0.00