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Hybrid Techniques based Speech Recognition

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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Year of Publication: 2016
Authors:
Ahlam Hanoon Shini, Zainab Ibrahim Abood, Tariq Ziad Ismaeel
10.5120/ijca2016909340

Ahlam Hanoon Shini, Zainab Ibrahim Abood and Tariq Ziad Ismaeel. Article: Hybrid Techniques based Speech Recognition. International Journal of Computer Applications 139(10):12-18, April 2016. Published by Foundation of Computer Science (FCS), NY, USA. BibTeX

@article{key:article,
	author = {Ahlam Hanoon Shini and Zainab Ibrahim Abood and Tariq Ziad Ismaeel},
	title = {Article: Hybrid Techniques based Speech Recognition},
	journal = {International Journal of Computer Applications},
	year = {2016},
	volume = {139},
	number = {10},
	pages = {12-18},
	month = {April},
	note = {Published by Foundation of Computer Science (FCS), NY, USA}
}

Abstract

Information processing has an important application which is speech recognition. In this paper, a two hybrid techniques have been presented. The first one is a 3-level hybrid of Stationary Wavelet Transform (S) and Discrete Wavelet Transform (W) and the second one is a 3-level hybrid of Discrete Wavelet Transform (W) and Multi-wavelet Transforms (M). To choose the best 3-level hybrid in each technique, a comparison according to five factors has been implemented and the best results are WWS, WWW, and MWM. Speech recognition is performed on WWS, WWW, and MWM using Euclidean distance (Ecl) and Dynamic Time Warping (DTW). The match performance is (98%) using DTW in MWM, while in the WWS and WWW are (74%) and (78%) respectively, but when using (Ecl) distance match performance is (62%) in MWM. So, in speech recognition to get the high alignment and high performance one must use DTW distance measurement.

References

  1. N. Trivedi., V. Kumar., S. Kumar, S. Ahuja, R. Chadha, “Speech Recognition by Wavelet Analysis”, International Journal of Computer Applications, Vol.15– No.8, Feb. 2011.
  2. S. B. Jr., R. C. Guido, L. S. Vieira, E. S. Fonseca, F. L. Sanchez, P.R. Scalassara, C. D. Maciel, J. C. Pereira and S. H. Chen, “Wavelet-based dynamic time warping”, Journal of Computational and Applied Mathematics 2009.
  3. Z. I. Abood, A. H. Al-sudani, “3-Level Techniques Comparison based Image Recognition”, International Journal of Computer Applications, Vol.97– No.11, July 2014.
  4. K R. Ghule, R. R. Deshmukh, “Feature Extraction Techniques for Speech Recognition: A Review”, International Journal of Scientific & Engineering Research, Vol. 6, Issue 5, May-2015.
  5. J. Sahaya, R. Alex, T. S. Shivkumar and N. Venkatesan, “Adapted DTW Joint with Wavelet Transform for Isolated Digit Recognition”, ARPN Journal of Engineering andApplied Sciences, Vol.10, No.1, Jan.2015.
  6. A. Chugh,P. Rana, S. Rana, “Speech Recognition System Using Wavelet Transform”, Research Article, International Journal of Computer Science and Mobile Computing, Vol. 3, Issue 8 Aug. 2014, PP 63-71.
  7. M. B. Martin and A. E. Bell, “New Image Compression Techniques using Multi-Wavelets and Multi-Wavelet Packets”, IEEE Transactions on Image Processing, Vol. 10, No. 4, Apr. 2001.
  8. S. Saminu, N. Özkurt, “Stationary Wavelet Transform and Entropy-Based Features for ECG Beat Classification”, International Journal of Research Studies in Science, Engineering and Technology Vol. 2, Issue 7, July 2015, PP 23-32.
  9. S. Bhatnagar and R. C. Jain, “A Comparative Analysis and Applications of MultiWavelet Transform in Image Design”, International Journal on Cybernetics & Informatics, Vol. 4, No. 2, Apr. 2015.
  10. S.R. Kodituwakku, U. S. Amarasinghe, “Comparison of Lossless Data Compression Algorithms for Text Data”, Indian Journal of Computer Science and Engineering, Vol 1 No. 4, PP 416-425.
  11. K. Kannan, S. A. Perumal, K. Arulmozhi, “Optimal Decomposition Level of Discrete, Stationary and Dual Tree Complex Wavelet Transform for Pixel based Fusion of Multi-focused Images”, Serbian Journal of Electrical Engineering, Vol. 7, No. 1, May 2010, PP 81-93.
  12. M. R Gamit, K. Dhameliya, “Isolated Words Recognition using MFCC, LPC and Neural Network”, International Journal of Research in Engineering and Technology, Vol.04 Issue: 06, June 2015.
  13. Z. I. Abood, I. J. Muhsin, N. J. Tawfiq, “Content-based Image Retrieval (CBIR) using Hybrid Technique”, International Journal of Computer Applications, Vol. 83 – No 12, Dec. 2013.
  14. M. Müller,m “Information Retrieval for Music and Motion”, Book, 2007.

Keywords

Hybrid techniques, speech recognition, multi-wavelet transform, wavelet transform, stationary wavelet transform, feature extraction, dynamic time warping.