• Aug 09, 2018 News![CFP] The annual meeting of IJFCC Editorial Board, ICCTD 2019, will be held in Prague, Czech Republic during March 2-4, 2019.   [Click]
  • Aug 09, 2018 News!IJFCC Vol. 6, No. 1-No. 3 has been indexed by EI (Inspec).   [Click]
  • Dec 24, 2018 News!The papers published in Vol.7, No.1-No.2 have all received dois from Crossref.
General Information
    • ISSN: 2010-3751
    • Frequency: Bimonthly (2012-2016); Quarterly (Since 2017)
    • DOI: 10.18178/IJFCC
    • Editor-in-Chief: Prof. Mohamed Othman
    • Executive Editor: Ms. Cherry L. Chan
    • Abstracting/ Indexing: Google Scholar,  Crossref, Electronic Journals LibraryEI (INSPEC, IET), etc.
    • E-mail:  ijfcc@ejournal.net 
Prof. Mohamed Othman
Department of Communication Technology and Network Universiti Putra Malaysia, Malaysia
It is my honor to be the editor-in-chief of IJFCC. The journal publishes good papers in the field of future computer and communication. Hopefully, IJFCC will become a recognized journal among the readers in the filed of future computer and communication.
IJFCC 2016 Vol.5(2): 125-129 ISSN: 2010-3751
doi: 10.18178/ijfcc.2016.5.2.458

Investigation of DNN-Based Keyword Spotting in Low Resource Environments

Kaixiang Shen, Meng Cai, Wei-Qiang Zhang, Yao Tian, and Jia Liu
Abstract—Keyword Spotting is a challenging task aiming at detecting the predefined keywords in utterances. In the low resource environment such as little keyword templates and the lack of linguistic information, the detection performance is always unsatisfactory. In this paper, we focus on the low resource situation where every keyword only has about 40 templates and the linguistic information is unknown. We explore using deep neural networks for acoustic modeling. In addition, we investigate several techniques including transfer-learning, multilingual bottleneck features, balancing keyword filler data and data augmentation to address the low resource problem and improve the system's performance. Compared with a query-by-example baseline system, substantial performance improvement can be obtained with our proposed keyword spotting system with deep neural network (KWS-DNN) framework.

Index Terms—Keyword spotting, DNN, acoustic model.

The authors are with the Department of Electronic Engineering, Tsinghua University, China (e-mail: skx13@mails.tsinghua.edu.cn, cai-m10@mails.tsinghua.edu.cn, wqzhang@tsinghua.edu.cn, chinaty188@163.com, ljia@tsinghua.edu.cn).


Cite: Kaixiang Shen, Meng Cai, Wei-Qiang Zhang, Yao Tian, and Jia Liu, "Investigation of DNN-Based Keyword Spotting in Low Resource Environments," International Journal of Future Computer and Communication vol. 5, no. 2, pp. 125-129, 2016.

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