In accordance with regulations and requirements, the editorial department's website domain has been changed to arocmag.cn. The original domain (arocmag.com) has been redirecting to new domain since Jan. 1st, 2025.
See More
Algorithm Research & Explore
|
App. Res. of Comp. Printed Article
Vol. 41, 2024 No. 12
3658-3663

Robust speech keyword spotting based on dual-branch fusion and time-frequency squeeze and excitation

Zhang Tingting
Qiu Zepeng
Zhao Lasheng
Mao Jiaying
Key Laboratory of Advanced Design & Intelligent Computing Ministry of Education, Dalian University, Dalian Liaoning 116622, China
DOI: 10.19734/j.issn.1001-3695.2024.04.0121

Abstract

In real-life scenarios, noise interferes with the temporal-frequency information of speech, leading to a decrease in the accuracy of keyword spotting models in noisy environments. To address this issue, this paper proposed a dual-branch fusion unit, which the temporal branch and the frequency branch extracted temporal and frequency features in parallel to reduce the information loss caused by serially stacking temporal and frequency convolutions. Cross-fusion enhanced the model's perception of temporal and frequency information, thereby it strengthened the model's feature representation capability. Additionally, this paper proposed a temporal-frequency squeeze and excitation module, which modeled the importance distribution of information in the temporal and frequency domains, enabling the model to selectively focus on valuable segments and further improved its robustness. Experimental results demonstrated that on the Google Command v2-12 dataset, the proposed model achieved higher recognition accuracy in tests with different signal-to-noise ratios compared to contrast models, while having a lower parameter count. Furthermore, the proposed model generalized better during testing for signal-to-noise ratio conditions that were not included during training. Experimental results show that the proposed model has advantages in recognition accuracy and parameter quantity, and has better noise robustness.

Foundation Support

辽宁省教育厅基本科研资助项目(LJKMZ20221838)
“111”计划资助项目(D23006)
大连市科技创新基金计划资助项目(2023JJ11CG002)

Publish Information

DOI: 10.19734/j.issn.1001-3695.2024.04.0121
Publish at: Application Research of Computers Printed Article, Vol. 41, 2024 No. 12
Section: Algorithm Research & Explore
Pages: 3658-3663
Serial Number: 1001-3695(2024)12-018-3658-06

Publish History

[2024-09-02] Accepted Paper
[2024-12-05] Printed Article

Cite This Article

张婷婷, 邱泽鹏, 赵腊生, 等. 基于双分支融合和时频压缩激励的鲁棒语音关键词识别 [J]. 计算机应用研究, 2024, 41 (12): 3658-3663. (Zhang Tingting, Qiu Zepeng, Zhao Lasheng, et al. Robust speech keyword spotting based on dual-branch fusion and time-frequency squeeze and excitation [J]. Application Research of Computers, 2024, 41 (12): 3658-3663. )

About the Journal

  • Application Research of Computers Monthly Journal
  • Journal ID ISSN 1001-3695
    CN  51-1196/TP

Application Research of Computers, founded in 1984, is an academic journal of computing technology sponsored by Sichuan Institute of Computer Sciences under the Science and Technology Department of Sichuan Province.

Aiming at the urgently needed cutting-edge technology in this discipline, Application Research of Computers reflects the mainstream technology, hot technology and the latest development trend of computer application research at home and abroad in a timely manner. The main contents of the journal include high-level academic papers in this discipline, the latest scientific research results and major application results. The contents of the columns involve new theories of computer discipline, basic computer theory, algorithm theory research, algorithm design and analysis, blockchain technology, system software and software engineering technology, pattern recognition and artificial intelligence, architecture, advanced computing, parallel processing, database technology, computer network and communication technology, information security technology, computer image graphics and its latest hot application technology.

Application Research of Computers has many high-level readers and authors, and its readers are mainly senior and middle-level researchers and engineers engaged in the field of computer science, as well as teachers and students majoring in computer science and related majors in colleges and universities. Over the years, the total citation frequency and Web download rate of Application Research of Computers have been ranked among the top of similar academic journals in this discipline, and the academic papers published are highly popular among the readers for their novelty, academics, foresight, orientation and practicality.

Indexed & Evaluation

  • The Second National Periodical Award 100 Key Journals
  • Double Effect Journal of China Journal Formation
  • the Core Journal of China (Peking University 2023 Edition)
  • the Core Journal for Science
  • Chinese Science Citation Database (CSCD) Source Journals
  • RCCSE Chinese Core Academic Journals
  • Journal of China Computer Federation
  • 2020-2022 The World Journal Clout Index (WJCI) Report of Scientific and Technological Periodicals
  • Full-text Source Journal of China Science and Technology Periodicals Database
  • Source Journal of China Academic Journals Comprehensive Evaluation Database
  • Source Journals of China Academic Journals (CD-ROM Version), China Journal Network
  • 2017-2019 China Outstanding Academic Journals with International Influence (Natural Science and Engineering Technology)
  • Source Journal of Top Academic Papers (F5000) Program of China's Excellent Science and Technology Journals
  • Source Journal of China Engineering Technology Electronic Information Network and Electronic Technology Literature Database
  • Source Journal of British Science Digest (INSPEC)
  • Japan Science and Technology Agency (JST) Source Journal
  • Russian Journal of Abstracts (AJ, VINITI) Source Journals
  • Full-text Journal of EBSCO, USA
  • Cambridge Scientific Abstracts (Natural Sciences) (CSA(NS)) core journals
  • Poland Copernicus Index (IC)
  • Ulrichsweb (USA)