Comparative Study of One Dimensional and Two Dimensional Dynamic Time Warping

Authors(2) :-Swathika R, Geetha K

Today's trend in Speech recognition applications include automatic answering machines, dictation systems, command control applications, speaker identification system etc. In this paper, early Patten matching technique DTW is studied used to find the similarity of speech data using MFCC and LPCC features. A small vocabulary containing command words used to test the already existing method in two ways. One-dimensional raw speech data of command words are considered as input the algorithm. In the second method, two-dimensional features of the same set of data were considered. Finally, these two methods were compared in terms of efficiency.

Authors and Affiliations

Swathika R
Department of Computer Science, Bharathiar University, Coimbatore, Tamilnadu, India
Geetha K
Department of Computer Science, Bharathiar University, Coimbatore, Tamilnadu, India

Mel Frequency Cepstral Coefficient, Linear Predictive cepstral Co-efficient, Dynamic time warping

  1. Palden Lama and Mounika Namburu. Speech Recognition with Dynamic Time Warping using MATLAB”, CS 525, SPRING 2010 – PROJECT  REPORT.
  2. Chunsheng Fang. From Dynamic Time Warping (DTW) to Hidden Markov Model (HMM), 2009/3/19,Final project report for ECE742 Stochastic Decision.
  3. Stan Salvador and Philip Chan. FastDTW: Toward Accurate Dynamic Time Warping in Linear Time and Space
  4. E- Hocine Bourouba. Mouldi Bedda and Rafik Djemili, Isolated Words Recognition System Based on Hybrid Approach DTW/GHMM, Informatica 30 (2006) 373–384 373.
  5. Rashid R.A and Mahalin N.H and Sarijari M.A and Abdul Aziz A.A. Security system using biometric technology: design and implementation of Voice Recognition System (VRS),International Conference on Computer and Communication Engineering, 2008.
  6. L.Muda and K.M Begam  and I. Elamvazuthi. Voice recognition algo¬rithms using Mel Frequency Cepstral Coefficient (MFCC) and Dynamic Time Warping (DTW) techniques, Journal of Computing, 2010, 2(3):138–143.
  7. DAVIS, S. & MERMELSTEIN, P. 1980. Comparison of parametric representations for monosyllabic word recognition in continuously spoken sentences. Acoustics,Speech and Signal Processing,IEEE Transactions on, 28, 357-366.
  8. Bhadragiri Jagan Mohan, Ramesh Babu. N, Speech Recognition using MFCC and DTW, https://www.researchgate.net/publication/260762671,    DOI: 10.1109/ICAEE.2014.6838564.
  9. L.Muda and K.M Begam  and I. Elamvazuthi. Voice recognition algo¬rithms using Mel Frequency Cepstral Coefficient (MFCC) and Dynamic Time Warping (DTW) techniques, Journal of Computing, 2010, 2(3):138–143
  10. Octavian, C., Abdulla, W. and Zoran, S. 2005, Performance Evaluation of Front-end Processing for Speech Recognition Systems.
  11. Rabiner, L.R., Shafer, R.W. 2009, Digital Processing of Speech Signals, 3rd edition, Pearson education in south Asia.
  12. BH Juang, On the hidden Markov model and dynamic time warping for speech recognition-A unified view, AT&T Tech Journal vol. 63, pp 1213-1243, 1984.
  13. S Nakagawa, Speaker-independent phoneme recognition in continuous speech by a statistical method and a stochastic dynamic time warping method, Tech Report CMU-CS-86-102, Carnegie Mellon University, 1985.
  14. Octavian Cheng, Waleed Abdulla, Zoran Salcic  Performance Evaluation of Front-end Processingfor Speech Recognition Systems School, 2005.
  15. Veton Kepuska. Speech Processing Project Linear Predictive coding using Voice excited Vecoder, ECE 5525, Osama Saraireh, Fall 2005.
  16. T. Levin and R. Pieraccini. Dynamic planar warping for optical character recognition. IEEE, International Conference on Acoustics, Speech, and Signal Processing, 3:149–152, 1992.   
  17. S. Uchida and H. Sakoe. A monotonic and continuous two-dimensional warping based on dynamic programming. In Proc. 14th International Conference on Pattern Recognition, volume 1, pages 521–524, 1998.      
  18. Daniel  Keysers  and  Walter Unger. Elastic image matching is np-complete. Pattern  Recogn. Lett., 24(1-3):445–453, 2003
  19. S. Uchida and  H. Sakoe. An approximation algorithm for two-dimensional warping, Institute of Electronics, Information, and Communication Engineers Transactions on Information & Systems, E83-D(1):109–111, 2000.
  20. H. Lei and V. Govindaraju. Direct image matching by dynamic warping. In Proc. of the 1st IEEE Workshop on Face Processing in Video, In conjunction with the IEEE International Conference on Computer Vision and Pattern Recognition (CVPR’04), Washington D.C., 2004.
  21. Titus Felix FURTUNA. Dynamic Programming Algorithms in Speech Recognition, Revista Informatica Economică nr. 2(46)/2008

Publication Details

Published in : Volume 2 | Issue 6 | November-December 2017
Date of Publication : 2017-12-31
License:  This work is licensed under a Creative Commons Attribution 4.0 International License.
Page(s) : 288-294
Manuscript Number : CSEIT172684
Publisher : Technoscience Academy

ISSN : 2456-3307

Cite This Article :

Swathika R, Geetha K, "Comparative Study of One Dimensional and Two Dimensional Dynamic Time Warping", International Journal of Scientific Research in Computer Science, Engineering and Information Technology (IJSRCSEIT), ISSN : 2456-3307, Volume 2, Issue 6, pp.288-294, November-December-2017. |          | BibTeX | RIS | CSV

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