Neural Architecture Search For Keyword Spotting

Abstract

Deep neural networks have recently become a popular solution to keyword spotting systems, which enable the control of smart devices via voice. In this paper, we apply neural architecture search to search for convolutional neural network models that can help boost the performance of keyword spotting based on features extracted from acoustic signals while maintaining an acceptable memory footprint. Specifically, we use differentiable architecture search techniques to search for operators and their connections in a predefined cell search space. The found cells are then scaled up in both depth and width to achieve competitive performance. We evaluated the proposed method on Google's Speech Commands Dataset and achieved a state-of-the-art accuracy of over 97% on the setting of 12-class utterance classification commonly reported in the literature.

Stats

• citations25
• S2 citations—
• github stars0
• HF likes0
• heat score10.61
• arxiv keymo2020neural

Related papers

• Autokws: Keyword Spotting With Differentiable Architecture Search (2020)9.92
• Deep Residual Learning For Small-footprint Keyword Spotting (2017)16.21
• End-to-end Keyword Spotting Using Neural Architecture Search And Quantization (2021)8.60
• Predicting Detection Filters For Small Footprint Open-vocabulary Keyword Spotting (2019)9.92
• Small-footprint Keyword Spotting With Graph Convolutional Network (2019)10.48
• An End-to-end Architecture For Keyword Spotting And Voice Activity Detection (2016)0.00
• Convmixer: Feature Interactive Convolution With Curriculum Learning For Small Footprint And Noisy Far-field Keyword Spotting (2022)12.61
• Efficient Keyword Spotting Using Time Delay Neural Networks (2018)10.21