CFP last date
20 December 2024
Call for Paper
January Edition
IJCA solicits high quality original research papers for the upcoming January edition of the journal. The last date of research paper submission is 20 December 2024

Submit your paper
Know more
The week's pick
Responsive image
Improved Shuffled Frog Leaping Algorithm with Self-Adaptive Shuffling for Fuzzy Logic PD+G Controller Optimization in Robotic Manipulators

Duc Hoang Nguyen
Random Articles
Reseach Article

Speech Feature Extraction and Classification: A Comparative Review

by Akansha Madan, Divya Gupta
journal cover thumbnail

International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 90 - Number 9
Year of Publication: 2014
Authors: Akansha Madan, Divya Gupta
10.5120/15603-4392

Akansha Madan, Divya Gupta . Speech Feature Extraction and Classification: A Comparative Review. International Journal of Computer Applications. 90, 9 ( March 2014), 20-25. DOI=10.5120/15603-4392

@article{ 10.5120/15603-4392,
author = { Akansha Madan, Divya Gupta },
title = { Speech Feature Extraction and Classification: A Comparative Review },
journal = { International Journal of Computer Applications },
issue_date = { March 2014 },
volume = { 90 },
number = { 9 },
month = { March },
year = { 2014 },
issn = { 0975-8887 },
pages = { 20-25 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume90/number9/15603-4392/ },
doi = { 10.5120/15603-4392 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T22:10:35.931141+05:30
%A Akansha Madan
%A Divya Gupta
%T Speech Feature Extraction and Classification: A Comparative Review
%J International Journal of Computer Applications
%@ 0975-8887
%V 90
%N 9
%P 20-25
%D 2014
%I Foundation of Computer Science (FCS), NY, USA
Abstract

This paper gives a brief survey on speech recognition and presents an overview for various techniques used at various stages of speech recognition systems. Researchers has been working in this research area for many years however accuracy for speech recognition still attention for variation of context, speaker's variability, environment conditions . The development of speech recognition system requires certain concepts to be included-Defining different classes of speech, techniques for speech feature extraction, speech classification modeling and measuring system performance . The main aim of this paper is to discuss and compare different approaches used for feature extraction and classification stages in speech recognition system.

References
  1. DOUGLAS O'SHAUGHNESSY, "Interacting With Computers by Voice: Automatic Speech Recognition and Synthesis", Proceedings of the IEEE, VOL. 91, NO 9, September 2003, 0018-9219/03$17. 00 © 2003 IEEE.
  2. O'Shaughnessy, D,"Interacting with computers by voice: automatic speech recognition and synthesis', Proc. IEEE, 2003, 91, (9), pp. 1272–1305.
  3. Corneliu Octavian DUMITRU, Inge GAVAT, "A Comparative Study of Feature Extraction Methods Applied to Continuous Speech Recognition in Romanian Language", 48th International Symposium ELMAR-2006, 07-09 June 2006, Zadar, Croatia.
  4. Muller, D. N. , de Siqueira, M. L. , Navaux, P. O. A. : 'A connectionist approach to speech understanding'. Int. Joint Conf. on Neural Networks, 2006 (IJCNN'06), Vancouver, BC, 2006, pp. 3790–3797
  5. Solera-Urena, R. , Padrell-Sendra, J. , Martin-Iglesias, D. , Gallardo-Antolin, A. , Pelaez-Moreno, C. , Diaz-De-Maria, F. : 'SVMs for automatic speech recognition: a survey', Progress in nonlinear speech processing (Springer-Verlag, Berlin, Heidelberg, 2007), pp. 190–216.
  6. Milone, D. H. , Di Persia, and L. E. : 'Learning hidden Markov models with hidden Markov trees as observation distributions'. Ninth Argentine Symp. Artificial Intelligence (ASAI 2007), Mar del Plata, Argentina, 2007, pp. 13–22.
  7. Mporas, I. , Ganchev, T. , Siafarikas, M. , Fakotakis, Comparison of speech features on the speech recognition task', J. Computer. Sci. , 2007, 3, (8), pp. 608–616
  8. M. Chandrasekar, M. Ponnavaikko, "Tamil speech recognition: a complete model", Electronic Journal «Technical Acoustics» 2008, 20.
  9. Korba, M. C. A. , Messadeg, D. , Djemili, R. H. B. : 'Robust speech recognition using perceptual wavelet denoising and Mel-frequency product spectrum cepstral coefficient features', Informatics, 2008, 32, pp. 283–288
  10. Xuefei, L. : 'A new wavelet threshold denoising algorithm in speech recognition '. Asia-Pacific Conf. on Information Processing, 2009(APCIP 2009), Shenzhen, 2009, pp. 310–313.
  11. Vimal Krishnan, V. R. , Babu Anto, P. : 'Features of wavelet packet decomposition and discrete wavelet transform for Malayalam speechrecognition', Recent Trends Eng. , 2009, 1, (2), pp. 93–96
  12. Zhou, P. , Tang, L. Z. , Xu, D. F. : 'Speech recognition algorithm of parallel sub band HMM based on wavelet analysis and neural network', Inf. Technol. J. , 2009, 8, pp. 796–800.
  13. M. A. Anusuya, S. K. Katti, Speech Recognition by Machine: A Review, (IJCSIS) International Journal of Computer Science and Information Security, 2009, Vol. 6, No. 3.
  14. Uma Maheswari, A. P. Kabilan, R. Venkatesh, "A Hybrid model of Neural Network Approach for Speaker independent Word Recognition", International Journal of Computer Theory and Engineering, Vol. 2, No. 6, December, 2010 1793-8201
  15. Anusuya, M. , Katti, S. : 'Front end analysis of speech recognition: a review', Int. J. Speech Technol. , 2011, 14, (2), pp. 99–145.
  16. Venkateswarlu, R. L. K. , Kumari, R. V. , Jayasri, G. V. : 'Speech recognition using radial basis function neural network'. Third Int. Conf. on Electronics Computer Technology (ICECT), 2011, Kanyakumari, 2011, pp. 441–445
  17. Sivaram, G. S. V. S. , Hermansky, H. : 'Multilayer perceptron with sparse hidden outputs for phoneme recognition'. 2011 IEEE Int. Conf. on Acoustics Speech and Signal Processing (ICASSP), Prague, 2011, pp. 5336–5339
  18. Vimala, C. , Radha, V. : 'A review on speech recognition challenges and approaches', World Computer. Sci. Inf. Technol. , 2012, 2, (1), pp. 1–7
  19. Ashok Shigli, Ibrahim Patel, A Spectral Feature Process for Speech Recognition Using HMM with MFCC Approach, National Conference on Computing and Communication Systems (NCCCS), 2012, 978-1-4673-1953-9
  20. Sharada C. Sajjan, Vijaya C, Comparison of DTW and HMM for Isolated Word Recognition, Proceedings of the International Conference on Pattern Recognition, Informatics and Medical Engineering, March 2012, 978-1-4673-1039, IEEE.
  21. Shing-Tai pan, Tzung-Pei Hong, Robust Speech Recognition by DHMM with A Codebook Trained by Genetic Algorithm, Journal of Information Hiding and Multimedia Signal Processing, October 2012, vol-3.
  22. Anjivani S. Bhabad, Gajanan K. Kharate, An Overview of Technical Progress in Speech Recognition, International Journal of Advanced Research in Computer Science and Software Engineering, March 2013,. Volume 3, Issue 3.
  23. Michelle Cutajar, Edward Gatt, Ivan Grech, Owen Casha, Joseph Micallef, Comparative study of automatic speech recognition techniques, IET Signal Process. , 2013, Vol. 7, Iss. 1, pp. 2.
Index Terms

Computer Science
Information Sciences

Keywords

Speech Recognition Robust speech recognition Speech feature extraction Classification

Powered by PhDFocusTM