Volume 12 Number 9 (Sep. 2017)
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JSW 2017 Vol.12(9): 732-743 ISSN: 1796-217X
doi: 10.17706/jsw.12.9.732-743

The Speech Recognition of Double-Syllable Chinese Words Based on the Hilbert Spectrum

Tianyang Long1, Long Zhang2, Tingfa Xu3, Shuangwei Wang1*
1School of Physics, Northeast Normal University, changchun, Jilin, China
2Liaohe Oilfield of China National Petroleum Corporation (Chaoyang) Gas Co., Ltd., Liaoning, China.
3School of Optoelectronics, Laboratory of Photoelectric Imaging and Information Engineering, Beijing Institute of Technology, China.


Abstract—Here a Chinese lexical recognition task is studied by a small vocabulary including 40 double-syllable Chinese words. In the approach presented, the Hilbert-Huang Transform (HHT) which consists of two steps is applied to speech signal analyzing. First, the speech signals are decomposed into a set of intrinsic mode functions (IMFs) by using the empirical mode decomposition (EMD) technique. Second, the first two IMFs are retained for further Hilbert spectral analysis. Final presentation of the speech signal is an energy-frequency-time distribution designated as the Hilbert spectrum, which can be used to depict the characteristics of speech sounds. For feature extraction, the Hilbert spectrum of each speech signal is divided into a set of frequency sub-bands. The number of discrete points on the Hilbert spectrum each sub-band contained is calculated as an element of the feature vector. Feature vectors obtained are fed to Support Vector Machine (SVM) classifier for classification. The proposed method is evaluated using 3840 speech samples from 8 different speakers (4 male). The experimental result, overall recognition rate of the 40 words achieving around 97% demonstrates the effectiveness of this approach.

Index Terms—Speech recognition, empirical mode decomposition, hilbert-huang transform, hilbert spectrum.

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Cite: Tianyang Long, Long Zhang, Tingfa Xu, Shuangwei Wang, "The Speech Recognition of Double-Syllable Chinese Words Based on the Hilbert Spectrum," Journal of Software vol. 12, no. 9, pp. 732-743, 2017.

General Information

ISSN: 1796-217X (Online)
Frequency:  Bimonthly 
Editor-in-Chief: Prof. Antanas Verikas
Executive Editor: Ms. Yoyo Y. Zhou
Abstracting/ Indexing: DBLP, EBSCO, CNKIGoogle Scholar, ProQuest, INSPEC(IET), ULRICH's Periodicals Directory, WorldCat, etc
E-mail: jsw@iap.org
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