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EdgeCRNN: an edge-computing oriented model of acoustic feature enhancement for keyword spotting

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Abstract

Keyword Spotting (KWS) is a significant branch of Automatic Speech Recognition (ASR) and has been widely used in edge computing devices. The goal of KWS is to provide high accuracy with a low False Alarm Rate (FAR), while reducing the costs of memory, computation, and latency. However, limited resources are challenging for KWS applications on edge computing devices. Lightweight models and structures for deep learning have achieved good results in the KWS branch while maintaining efficient performances. In this paper, we present a new Convolutional Recurrent Neural Network (CRNN) architecture named EdgeCRNN for edge computing devices. EdgeCRNN, which is based on depthwise separable convolution and residual structure, uses a feature enhanced method. On the Google Speech Commands Dataset, the experimental results depict that EdgeCRNN can test 11.1 audio data per second on Raspberry Pi 3B+, which is 2.2 times than that of Tpool2. Compared with Tpool2, the accuracy of EdgeCRNN reaches 98.05% whilst its performance is also competitive.

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Authors and Affiliations

  1. School of Computer Science, South China Normal University, Guangzhou, China

    Yungen Wei, Zheng Gong, Shunzhi Yang & Kai Ye

  2. Computer Engineering Technical College, GuangDong Polytechnic of Science and Technology, Guangzhou, China

    Yungen Wei, Shunzhi Yang & Kai Ye

  3. School of Statistics and Mathematics, Guangdong University of Finance and Economics, Guangzhou, China

    Shunzhi Yang, Kai Ye & Yamin Wen

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  1. Yungen Wei
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  2. Zheng Gong
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Correspondence to Yamin Wen.

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A preliminary version of this paper has been published at ML4CS 2020, Springer LNCS, this is the full-length version. This paper is supported by the National Natural Sciences Foundation of China (No. 62072192), National Cryptography Development Fund (No. MMJJ20180206), the Project of Science and Technology of Guangzhou (No. 201802010044), Guangdong Basic and Applied Basic Research Foundation (No. 2019A1515011797), the Opening Project of GuangDong Province Key Laboratory of Information Security Technology(No. 2020B1212060078), the Project of Guangdong Province Innovative Team(2020WCXTD011) and the Research Team of Big Data Audit from Guangdong University of Finance and Economics.

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Wei, Y., Gong, Z., Yang, S. et al. EdgeCRNN: an edge-computing oriented model of acoustic feature enhancement for keyword spotting. J Ambient Intell Human Comput 13, 1525–1535 (2022). https://doi.org/10.1007/s12652-021-03022-1

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