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Speech Recognition System and Isolated Word Recognition based on Hidden Markov Model (HMM) for Hearing Impaired
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 73 - Number 20 |
| Year of Publication: 2013 |
| Authors: S. Ananthi, P. Dhanalakshmi |
10.5120/13012-0241
|
S. Ananthi, P. Dhanalakshmi . Speech Recognition System and Isolated Word Recognition based on Hidden Markov Model (HMM) for Hearing Impaired. International Journal of Computer Applications. 73, 20 ( July 2013), 30-34. DOI=10.5120/13012-0241
Abstract
The ability of a reader to recognize written words correctly, virtually and effortlessly is defined asWord Recognition or Isolated Word Recognition. It will recognize each word from their shape. Speech Recognition is the operating system which enablesto convert spoken words to written text which is called as Speech to Text (STT) method. Usual Method used in Speech Recognition (SR) is Neural Network, Hidden Markov Model (HMM) and Dynamic Time Warping (DTW). The widely used technique for Speech Recognition is HMM. Hidden Markov Model assumes that successive acoustic features of a spoken word are state independent. The occurrence of one feature is independent of the occurrence of the others state. Here each single unit of word is considered as state. Based upon the probability of the state it generates possible word sequence for the spoken word. Instead of listening to the speech, the generated sequence of text can be easily viewed. Each word is recognized from their shape. People with hearing impaired can make use of this Speech Recognition.
References
- Language: Implications for deaf readers. Journal of Deaf Studies and Deaf Education5(1), pages 32–50, 2000. Winter.
- Hocine Bourouba, Mouldi Bedda, and Rafik Djemili. Isolated word recognition based on hybrid approach dtw/ghmm. INFORMATICA 30, pages 373–384, March 2006.
- Horia Cucu, Andi Buzo, and Corneliu Burileanu. Optimization methods for large vocabulary, isolated word recognition in romanian language. U. P. B. Sci. Bull. , Series C, 73(2):179–192, 2011. ISSN:1154-234x.
- Baum. L. E and Petrie T. Statistical inference for probabilistic functions of finite state markov chains. Ann. Mathemat. Stat. , pages 37: 1554–1563, 1996.
- Jelinek. F. Statistical methods for speech recognition. MIT Press, 1998. Mass.
- Edward Gatt, Joseph Micallef, Paul Micsllef, and Edward Chilton. Phoneme classification in hardware implemented neural networks. IEEE trans, page 481, 2001.
- Bahlmann. Haasdonk. and Burkhardt. speech and audio recognition. IEEE trans. , 11, May 2003.
- Heab-Umbach and H. Ney. Linear discriminant analysis for improved large vocabulary continuous speech recognition. Proc. of International Conference on. Acoustics, Speech and Signal Processing, 73:13–16, 1992.
- Fong Huang and Frank K. Soong. A new discriminative hmm training procedure. Journal of Acoustic Society of America in 118th Meeting, pages 481–484, October 1999.
- Blimes J. A gentle tutorial on the em algorithm and its application to parameter estimation for gaussian mixture and hidden markov models. Technical Report, April 1998. University of Berkeley.
- Rabiner. L. A tutorial on hidden markov model and selected applications in speech recognition. Proc. of IEEE 37, page 257.
- Rabiner L. and Juang B. H. Fundamentals of speech recognition, prentice-hall. Englewed cliffs N. J. , 1993.
- Lori F. Lamel, Lawrence R. Rabiner, Aaron E. Rosenberg, and Jay G. Wilpon. An improved endpoint for isolated word recognition. IEEE Trans. on Acoustics, Speech and Signal Processing, ASSP-29(4):777–785, August 1981.
- Baum L. E. An inequality and associated maximization technique in statistical estimation for probabilistic functions of markov processes. in equalities, 3:1–8, 1972.
- Antanas Lipeika, Joana Lipeikiene, and Laimutis Telksnys. Development of isolated word recognition system. INFORMATICA, 13(1):37–46, 2002.
- Ferrer M. A. , Camino J. L, Travieso C. M. , and Morales C. Signature classification by hmm. IEEE International Carnahan Conf. on Security Technology (IEEE ICCST'99), pages 481–484, October 1999.
- Linga Murthy M. K and Murthy G. L. N. Isolated word recognition using lpc and vector quantization. International Journal of Research in Engineering and Technology( IJRET), pages 479–482, November 2012.
- Linga Murthy M. K and Murthy G. L. N. Recognition isolated word using features based on lpc, mfcc, zcr and ste with network classifiers. International Journal of Modern Engineering Research (IJMER), 2(3):854–858, May-June 2012.
- Bellman R. and Dreyfus S. Applied dynamic programming. NJ:Princeton University Press, 1962.
- Raudys S. and Duin P. W. Optimization methods for large vocabulary, isolated word recognition in romanian language. Pattern Recognition Letters, 19:385–392, 1998.
- Reynals S. , Morgan N. , Bourland H. , and Franco R. Connectionist probability estimators in hmm speech recognition. IEEE Trans. on Speech and Audio Processing 2(1), pages 161–174, 1994.
- Levinson S. E. Continuously variable duration hidden markov model for speech analysis. Proc. ICASSP, pages 1241–1244, 1986.
- Leitch D. and MacMillan T. How students with disabilities respond to speech recognition technology in the university classroom. Year III Final Research Report on the Liberated Learning Project, July 2000.
- Linde Y. , Buzo A. , and Gray R. M. An algorithm for vector quantizer design. IEEE Trans. COM -28, January 1980.
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