Low Parameter and FLOPs1Speech Densely Connected Convolutional Networks for Keyword Spotting

Tsung Han Tsai; Xin Hui Lin

doi:10.1109/ICCE-Taiwan55306.2022.9869290

Low Parameter and FLOPs¹Speech Densely Connected Convolutional Networks for Keyword Spotting

Tsung Han Tsai
, Xin Hui Lin

Department of Electrical Engineering

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

Abstract

Due to the fast-paced nature of technology, people focus on keyword spotting technology for the use of human-computer interaction (HCI). In this paper, a keyword spotting technique based on the Convolutional neural network (CNN) method is proposed. This network model is modified with the densely connected convolutional network (DenseNet) and uses grouped convolution and deep separable convolution to construct complete keyword spotting tasks. Besides, we change the width and depth of the network to construct a compact variation of the network. We established the network using the Google Speech Command Dataset V2. Compared to different networks, our proposed network sacrifices a small quantity of precision to have a low number of parameters and floating-point operations (FLOPs).

Original language	English
Title of host publication	Proceedings - 2022 IEEE International Conference on Consumer Electronics - Taiwan, ICCE-Taiwan 2022
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	271-272
Number of pages	2
ISBN (Electronic)	9781665470506
DOIs	https://doi.org/10.1109/ICCE-Taiwan55306.2022.9869290
State	Published - 2022
Event	2022 IEEE International Conference on Consumer Electronics - Taiwan, ICCE-Taiwan 2022 - Taipei, Taiwan Duration: 6 Jul 2022 → 8 Jul 2022

Publication series

Name	Proceedings - 2022 IEEE International Conference on Consumer Electronics - Taiwan, ICCE-Taiwan 2022

Conference

Conference	2022 IEEE International Conference on Consumer Electronics - Taiwan, ICCE-Taiwan 2022
Country/Territory	Taiwan
City	Taipei
Period	6/07/22 → 8/07/22

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 7 Affordable and Clean Energy

Access to Document

10.1109/ICCE-Taiwan55306.2022.9869290

Cite this

Tsai, T. H., & Lin, X. H. (2022). Low Parameter and FLOPs¹Speech Densely Connected Convolutional Networks for Keyword Spotting. In Proceedings - 2022 IEEE International Conference on Consumer Electronics - Taiwan, ICCE-Taiwan 2022 (pp. 271-272). (Proceedings - 2022 IEEE International Conference on Consumer Electronics - Taiwan, ICCE-Taiwan 2022). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICCE-Taiwan55306.2022.9869290

Tsai, Tsung Han ; Lin, Xin Hui. / Low Parameter and FLOPs¹Speech Densely Connected Convolutional Networks for Keyword Spotting. Proceedings - 2022 IEEE International Conference on Consumer Electronics - Taiwan, ICCE-Taiwan 2022. Institute of Electrical and Electronics Engineers Inc., 2022. pp. 271-272 (Proceedings - 2022 IEEE International Conference on Consumer Electronics - Taiwan, ICCE-Taiwan 2022).

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title = "Low Parameter and FLOPs1Speech Densely Connected Convolutional Networks for Keyword Spotting",

abstract = "Due to the fast-paced nature of technology, people focus on keyword spotting technology for the use of human-computer interaction (HCI). In this paper, a keyword spotting technique based on the Convolutional neural network (CNN) method is proposed. This network model is modified with the densely connected convolutional network (DenseNet) and uses grouped convolution and deep separable convolution to construct complete keyword spotting tasks. Besides, we change the width and depth of the network to construct a compact variation of the network. We established the network using the Google Speech Command Dataset V2. Compared to different networks, our proposed network sacrifices a small quantity of precision to have a low number of parameters and floating-point operations (FLOPs).",

author = "Tsai, \{Tsung Han\} and Lin, \{Xin Hui\}",

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Tsai, TH & Lin, XH 2022, Low Parameter and FLOPs¹Speech Densely Connected Convolutional Networks for Keyword Spotting. in Proceedings - 2022 IEEE International Conference on Consumer Electronics - Taiwan, ICCE-Taiwan 2022. Proceedings - 2022 IEEE International Conference on Consumer Electronics - Taiwan, ICCE-Taiwan 2022, Institute of Electrical and Electronics Engineers Inc., pp. 271-272, 2022 IEEE International Conference on Consumer Electronics - Taiwan, ICCE-Taiwan 2022, Taipei, Taiwan, 6/07/22. https://doi.org/10.1109/ICCE-Taiwan55306.2022.9869290

Low Parameter and FLOPs¹Speech Densely Connected Convolutional Networks for Keyword Spotting. / Tsai, Tsung Han; Lin, Xin Hui.
Proceedings - 2022 IEEE International Conference on Consumer Electronics - Taiwan, ICCE-Taiwan 2022. Institute of Electrical and Electronics Engineers Inc., 2022. p. 271-272 (Proceedings - 2022 IEEE International Conference on Consumer Electronics - Taiwan, ICCE-Taiwan 2022).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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N2 - Due to the fast-paced nature of technology, people focus on keyword spotting technology for the use of human-computer interaction (HCI). In this paper, a keyword spotting technique based on the Convolutional neural network (CNN) method is proposed. This network model is modified with the densely connected convolutional network (DenseNet) and uses grouped convolution and deep separable convolution to construct complete keyword spotting tasks. Besides, we change the width and depth of the network to construct a compact variation of the network. We established the network using the Google Speech Command Dataset V2. Compared to different networks, our proposed network sacrifices a small quantity of precision to have a low number of parameters and floating-point operations (FLOPs).

AB - Due to the fast-paced nature of technology, people focus on keyword spotting technology for the use of human-computer interaction (HCI). In this paper, a keyword spotting technique based on the Convolutional neural network (CNN) method is proposed. This network model is modified with the densely connected convolutional network (DenseNet) and uses grouped convolution and deep separable convolution to construct complete keyword spotting tasks. Besides, we change the width and depth of the network to construct a compact variation of the network. We established the network using the Google Speech Command Dataset V2. Compared to different networks, our proposed network sacrifices a small quantity of precision to have a low number of parameters and floating-point operations (FLOPs).

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Tsai TH, Lin XH. Low Parameter and FLOPs¹Speech Densely Connected Convolutional Networks for Keyword Spotting. In Proceedings - 2022 IEEE International Conference on Consumer Electronics - Taiwan, ICCE-Taiwan 2022. Institute of Electrical and Electronics Engineers Inc. 2022. p. 271-272. (Proceedings - 2022 IEEE International Conference on Consumer Electronics - Taiwan, ICCE-Taiwan 2022). doi: 10.1109/ICCE-Taiwan55306.2022.9869290