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Reseach Article

Classification of the Spoken Hindi Partially Reduplicated Words using Artificial Neural Network

by Varsha Gupta, Anuj Sharma
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 93 - Number 10
Year of Publication: 2014
Authors: Varsha Gupta, Anuj Sharma
10.5120/16248-5845

Varsha Gupta, Anuj Sharma . Classification of the Spoken Hindi Partially Reduplicated Words using Artificial Neural Network. International Journal of Computer Applications. 93, 10 ( May 2014), 1-6. DOI=10.5120/16248-5845

@article{ 10.5120/16248-5845,
author = { Varsha Gupta, Anuj Sharma },
title = { Classification of the Spoken Hindi Partially Reduplicated Words using Artificial Neural Network },
journal = { International Journal of Computer Applications },
issue_date = { May 2014 },
volume = { 93 },
number = { 10 },
month = { May },
year = { 2014 },
issn = { 0975-8887 },
pages = { 1-6 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume93/number10/16248-5845/ },
doi = { 10.5120/16248-5845 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T22:15:25.353395+05:30
%A Varsha Gupta
%A Anuj Sharma
%T Classification of the Spoken Hindi Partially Reduplicated Words using Artificial Neural Network
%J International Journal of Computer Applications
%@ 0975-8887
%V 93
%N 10
%P 1-6
%D 2014
%I Foundation of Computer Science (FCS), NY, USA
Abstract

The most ordinary way of information exchange is Speech. It provides an efficient way of man-machine communication using speech interfacing. Speech interfacing involves two process, speech synthesis and speech recognition. Speech recognition allows a computer to identify the words that a person speaks to a microphone or telephone. The two main mechanism, used in speech recognition, are signal processing mechanism at front-end and pattern matching mechanism at back-end. In this paper, a setup for recognition of Spoken Hindi Partially Reduplicated Words (SHPRW), that uses Mel frequency cepstral coefficients at front-end and artificial neural networks at back-end has been developed to perform the experiment.

References
  1. Preeti saini, Parneet kaur, 'Automatic Speech Recognition: A Review', International Journal of Engineering Trends and Technology- Volume4Issue2- 2013.
  2. Hisashi Wakita, 1977, Normalization of Vowels by Vocal Tract Length and Its Applications to Vowel Identification, IEEE Transactions on Acoustics, Speech and Signal Processing, Vol. 25.
  3. Shasidhar G. Koolagudi, Ramu Reddy, Jainath Yadav and K. Sreenivasa Rao, 2011, IITKGP-SEHSC: Hindi speech corpous for emotion analysis, IEEE International Conference on Devices and Communications
  4. R. Cowie and R. R. Cornelius, 2003, Describing the emotional states that are expressed in speech, Speech Communication, Elsevier, Vol. 40.
  5. Dinesh Kumar Rajoriya, R. S. Anand & R. P. Maheshwari, 2011, Spoken Paired Word Pattern Classification Using Whole Word Template, TECHNIA- International Journal of Computing Science and Communication Technologies, Vol. 3.
  6. A. K. Jain, R. P. W. Duin, and J. Mao, 2000, Statistical pattern recognition: A Review, IEEE Transaction on Pattern Analysis and Machine Intelligence, Vol. 22.
  7. Dinesh Kumar Rajoriya, R. S. Anand & R. P. Maheshwari, 2011, Enhanced recognition rate of spoken Hindi paired word using probabilistic neural network approach, International Journal of Information and Communication Technology, Inderscience Publishers, Geneva, Switzerland, Vol. 3.
  8. Hariharan R. , Hakkinan J. and Laurila K. , 2001, Robust end of utterance detection for real time speech recognition applications, IEEE International conference on Acoustics, Speech and Signal Processing.
  9. A. K. Jain, R. P. W. Duin, and J. Mao, 2000, Statistical pattern recognition: A Review, IEEE Transaction on Pattern Analysis and Machine Intelligence, Vol. 22.
  10. Anand Singh, Dr. Dinesh Kumar Rajoriya and Vikash Singh, 2012, Database Development and Analysis of Spoken Hindi Hybrid Words Using Endpoint Detection, International Journal of Electronics and Computer Science Engineering, Vol. 1.
  11. Rabiner, L. R. and Levinson, S. E. , 1981, Isolated and connected word recognition theory and selected applications, IEEE Transactions on Communications, Vol. 29.
  12. S. Davis and P. Mermelstein, "Comparison of parametric representations for monosyllabic word recognition in continuously spoken sentences", IEEE Trans. ASSP-28, pp 357-366, 1980.
  13. 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, Volume 2, Issue 3, March 2010.
  14. http://www. cse. unsw. edu. au/~waleed/phd/html/node38. html, downloaded on 1st March 2014.
  15. Jamal Price, sophomore student, Design an automatic speech recognition system using maltab, University of Maryland Estern Shore Princess Anne.
  16. V. Tabarabaee, B. Azimisadjadi, S. B. Zahirazami and C. Lucas, 1994, Isolated word recognition using a hybrid neural network, IEEE International conference on Acoustics, Speech and Signal Processing.
  17. Sonia Sunny, David Peter S. , K. Poulose Jacob, 2011, Wavelet Packet Decomposition and Artificial Neural Networks based Recognition of Spoken Digits, International journal of machine intelligence, Vol. 3.
  18. Petek, B. and Tebelskis, J. (1992). Context- Dependent Hidden Control Neural Network Architecture for Continuous Speech Recognition. In Proceeding IEEE International Conference on Acoustics, Speech and Signal Processing.
  19. Sonia Sunny, David Peter S. , K. Poulose Jacob, 2011, Wavelet Packet Decomposition and Artificial Neural Networks based Recognition of Spoken Digits, International journal of machine intelligence, Vol. 3.
  20. Demuth, H. , Beale, M. and Hagan, M. , 2008, Neural network toolbox 6 user's guide, Mathworks Tool Box.
  21. Anand Singh, Dinesh Kumar Rajoriya, Vikash Singh, ' Broad Acoustic Classification of Spoken Hindi Hybrid Paired Words using Artificial Neural Networks', International Journal of Computer Applications (0975 – 8887) Volume 52– No. 12, August 2012.
  22. Mehta, K. and Anand, R. S. , 2010, Robust front-end and back-end processing for feature extraction for Hindi Speech recognition, IEEE International Conference on (ICCIC).
  23. Vibha Tiwari, 'MFCC and its applications in speaker recognition', International Journal on Emerging Technologies 1(1): 19-22(2010).
  24. Deeksha Bhatnagar, Vikash Singh, Sandip Vijay, ' Database Enhancement and Analysis of Spoken Hindi Reduplicated Words using Endpoint Detection Algorithm', International Journal of Computer Applications (0975 – 8887) Volume 63– No. 9, February 2013.
  25. Varsha Gupta, Mukul Pant, 'An approach to describe methods Of front end processing of Speech signal', International Journal of Scientific & Engineering Research Volume 4, Issue3, March-2013.
  26. Schulze, E. , 1982, Hypothesizing of words for isolated and connected word recognition systems based on phonem preclassification, IEEE International conference on Acoustics, Speech and Signal Processing.
Index Terms

Computer Science
Information Sciences

Keywords

Automatic Speech Recognition (ASR) Spoken Hindi Partially Reduplicated Words (SHPRW) Endponit Detection (EPD) Mel Frequency Cepstral Component (MFCC) and Artificial Neural Network (ANN).