MLFNN algorithm for speech classification in electrical wheelchair control for disabled people

Abstract: 143 | PDF: 80

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Author

  • Nguyen Thanh Hai*

Keywords:

MFCC feature extraction
Speech classification
5 speech commands
Hamming lowpass filter
Multiplayer Feedforward Neural Networks.

Abstract

This paper proposes a Multilayer Feedforward Neural Network (MLFNN) algorithm for speech classification in electrical wheelchair control in which the feature extraction of speech signals is performed using a Mel Frequency Cepstral Coefficients (MFCC) method. The speech signals are the control commands recognized such as Left, Right, Forward, Backward and Stop. In addition, a Hamming lowpass filter is applied to frame and reduce noise before feature extraction. Therefore, the recognized signals will be used to control the electrical wheelchair. Experimental results of 30 time experiments on an electrical wheelchair produced the accuracy from 85% to 90%. This study can support disabled people in moving their wheelchairs easily and more conveniently in everyday life and also shows the effectiveness of the proposed approach.

References

    [1] Gunda Gautam, Gunda Sumanth, Karthikeyan K C, Shyam Sundar, D.Venkataraman “Eye Movement Based Electronic Wheel Chair For Physically Challenged Persons” International Journal of Scientific & Technology Research Vol. 3, No. 2, 2014.
    [2] Mohamed Fezari and Ibrahiem M. El-Emary “Speech and Sensors in Improving the Guidance of an Electric Wheelchair by Handicapped Persons” Proceedings of the 2014 International Conference on Circuits, Systems and Control, 2014.
    [3] Bhupinder Singh, Neha Kapur, Puneet Kaur, “Speech Recognition with Hidden Markov Model: A Review”, International Journal of Advanced in Computer Science and Soft Engineering, Vol. 2, pp. 400-403, 2012.
    [4] Nguyen Tu ha, Pham Van Tuan, “Design of Television Remote Control by Vietnamese Speech”, Tạp chí Khoa học và Công nghệ, Đại học Đà Nẵng, 2014.
    [5] Dipmoy Gupta, Radha Mounima C. Navya Manjunath, Manoj PB, “Isolated Word Speech Recognition Using Vector Quantization”, International Journal of Advanced in Computer Science and Soft Engineering, Vol. 2, pp. 164-168, 2012.
    [6] Talal Bin Amin, Iftekhar Mahmood, “Speech Recognition Using Dynamic Time Warping”, ICAST, 2008.
    [7] A.Revathi, R.Ganapathy and Y.Venkataramani, “Text Independent Speaker Recognition and Speaker Independent Speech Recognition Using Iterative Clustering Approach”, International Journal of Computer science & Information Technology, Vol. 1, No 2, pp. 30-42, 2009.
    [8] Lindasalwa Muda, Mumtaj Begam and I. Elamvazuthi, “Voice Recognition Algorithms using Mel Frequency Cepstral Coefficient (MFCC) and Dynamic Time Warping (DTW) Techniques”, Journal Of Computing, Vol. 2, Issue 3, pp. 138-143, 2010.
    [9] Kohei Arai, Ronny Mardiyanto, Eyes Based Eletric Wheel Chair Control System, International Journal of Advanced Computer Science and Applications, 2011.
    [10] S. M. Azam, Z.A. Mansoor, M. Shahzad Mughal, S. Mohsin, “Urdu Spoken Digits Recognition Using Classified MFCC and Backpropgation Neural Network”, Computer Graphics, Imaging and Visualisation IEEE, Vol.1, pp. 7695-2928, 2007.
    [11] Chin Luh Tan and Adznan Jantan, “Digit Recognition Using Neural Networks”, Malaysian Journal of Computer Science, Vol. 17, No. 2, pp. 40-54, 2004.
    [12] R. Rojas, Neural Networks. Berlin: Springer-Verlag, 1996.
    [13] Fu-Hua Liu; Richard M. Stern; Xuedong Huang; Alejandro Acero, “Efficient cepstral normalization for robust speech recognition, human language technology”, Proceedings of a Workshop Held at Plainsboro, pp. 21-24, 1993.
    [14] Z. Hachkar et al., “A Comparison of DHMM and DTW for Isolated Digits Recognition System of Arabic Language”, International J. on Computer Science and Enginerring, 2011.
    [15] Tran Duc Minh, Nguyen Thien Luan, “Using Fuzzy Logic in Vietnamese Speech Recognition”, Tạp chí Khoa học và Công nghệ, Đại học Đà Nẵng, 2014.
    [16] Anoop.K.J, Inbaezhilan, Sathish raj, Ramaseenivasan, CholaPandian, “Designing and Modeling of Voice Controlled Wheel Chair Incorporated with Home Automation” International Journal of Advanced Research in Electrical, Electronics and Instrumentation Engineering, Vol. 17, No. 2, pp. 53-59, 2014.
    [17] Mohamed Fezari and Ibrahiem M. El-Emary, “Speech and Sensors in Improving the Guidance of an Electric Wheelchair by Handicapped Persons”, Proceedings of the International Conference on Circuits, Systems and Control, 2014.
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Published

Sep 30, 2015
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How to Cite
Nguyen Thanh Hai*. “MLFNN Algorithm for Speech Classification in Electrical Wheelchair Control for Disabled People”. The University of Danang - Journal of Science and Technology, vol. 9, no. 94, Sept. 2015, pp. 23-27, https://jst-ud.vn/jst-ud/article/view/2321.

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