International Journal of Computer
Trends and Technology

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Volume 67 | Issue 5 | Year 2019 | Article Id. IJCTT-V67I5P131 | DOI : https://doi.org/10.14445/22312803/IJCTT-V67I5P131

Speech Recognition performance of Dental Nasal an Retroflex nasal Phonomes of Malayalam Language

Cini Kurian

Citation :

Cini Kurian, "Speech Recognition performance of Dental Nasal an Retroflex nasal Phonomes of Malayalam Language," International Journal of Computer Trends and Technology (IJCTT), vol. 67, no. 5, pp. 189-192, 2019. Crossref, https://doi.org/10.14445/22312803/ IJCTT-V67I5P131

Abstract

Interaction with computer through a convenient and user-friendly interface has always been an important technological issue. Machine-oriented interfaces [1 ] restrict the computer usage to a minuscule fraction of the population, who are both computer literate and conversant with written English. Computers which can recognize speech in native languages enable common man to make use of the benefits of information technology. In this paper speech recognition performance of Dental Nasal and Retroflex Nasal phonemes of Malayalam language have been explored.

Keywords

Automatic Speech Recognition, Malayalam language

References

[1] Syama, R. and Mary Idicula, S., “Speech Recognition for Malyalam Language”, 2008.
[2] Vimal Krishnan V.R Athulya Jayakumar Babu Anto.P -2008 , Speech Recognition of Isolated Malayalam Words Using Wavelet Features and Artificial Neural Network -
[3] Raji Sukumar.A Firoz Shah.A Babu Anto.P -2010- Isolated question words Recognition from speech queries by Using artificial neural networks
[4] Sonia Sunny, David Peter S., and K Poulose Jacob A Comparative Study of Wavelet Based Feature Extraction Techniques in Recognizing Isolated Spoken Words," International Journal of Signal Processing Systems, Vol.1, No.1, pp. 49-53, June 2013.doi: 10.12720/ijsps.1.1.49-53
[5] Continuous Malayalam speech recognition using Hidden Markov Models.Anuj Mohamed, K. N. Ramachandran Nair Proceedings of the 1st Amrita ACM-W Celebration on Women in Computing in India, September 16-17, 2010, Tamilnadu, India; 01/2010
[6] SUNNY, SONIA; DAVID PETER, S.; POULOSE JACOB, K. "RECOGNITION OF SPEECH SIGNALS: AN EXPERIMENTAL COMPARISON OF LINEAR PREDICTIVE CODING AND DISCRETE WAVELET TRANSFORMS"International Journal of Engineering Science & Technology;Apr2012, Vol. 4 Issue 4, p1594
[7] SUNNY, SONIA; DAVID PETER, S.; POULOSE JACOB, K Discrete Wavelet Transforms and Artificial Neural Networksfor Recognition of Isolated Spoken Words International Journal of Computer Applications (0975 – 8887) Volume 38– No.9, January 2012
[8] Rabiner, L. Juang, B. H., Yegnanarayana, B., “Fundamentals of Speech Recognition”, Pearson Publishers, 2010.
[9] Felinek, “Statistical Methods for Speech recognition” MIT Press, cambridge assachusetts, USA, 1997
[10] Hwang, M. Y. (1993). Sub-phonetic acoustic modeling for speaker-independentcontinuous speech recognition. Ph.D. Thesis. Carnegie Mellon University.