Comparison of Palateral, Retroflex and Alvelor Lateral in Automatic Speech Recognition
|International Journal of Engineering Trends and Technology (IJETT)||
|© 2019 by IJETT Journal|
|Year of Publication : 2019|
|Authors : Cini Kurian
|DOI : 10.14445/22315381/IJETT-V67I5P217|
MLA Style: Cini Kurian "Comparison of Palateral, Retroflex and Alvelor Lateral in Automatic Speech Recognition" International Journal of Engineering Trends and Technology 67.5 (2019):111-114.
APA Style: Cini Kurian (2019). Comparison of Palateral, Retroflex and Alvelor Lateral in Automatic Speech Recognition International Journal of Engineering Trends and Technology,67(5),111-114.
Speaking with the machine to achieve desired task , make the modern devices easier and convenient to use. Although may interactive software applications are available, the use these applications are limited due to language barriers. Hence development of speech recognition systems in local languages will help anyone to make use of this technology. In this paper Speech Recognition performance of three important phonemes of Malayalam Language – Palateral Lateral , Retroflex lateral and Alvelor Lateral have been analyzed.
 Balaji. V., K. Rajamohan, R. Rajasekarapandy, S. Senthilkumaran,"Towards a knowledge system for sustainable food security: The information village experiment in Pondicherry," in IT Experience in India : Bridging the Digital Divide, Kenneth Keniston and Deepak Kumar, eds., New Delhi, Sage,2004.
 Bhaskararao P., “Salient phonetic features of Indian languages”, Sadhana, 36(5), pp. 587-599, Oct. 2011.
 J. Holmes (1988). Speech synthesis and recognition. Van Nostrand Reinhold (UK) Co. Ltd., Wokingham
 B. H. Juang & Lawrence R. Rabiner. Automatic Speech Recognition -- A Brief History of the Technology Development. Georgia Institute of Technology. Atlanta Rutgers University and the University of California. Santa Barbara.
 Jurafsky, Daniel, and James H. Martin. 2009. Speech and Language Processing: An Introduction to Natural Language Processing, Speech Recognition, and Computational Linguistics. 2nd edition. Prentice-Hall.
 Furui, S., “50 Years of Progress in Speech and Speaker Recognition Research Identification”, In ECTI Transformations on Computer and Information Technology, vol. 1, no. 2, 2003
 Wiqas Ghai , Navdeep Singh "Literature Review on Automatic Speech Recognition" International Journal of Computer Applications (0975 – 8887) Volume 41– March 2012.
 S. Young (1999). Acoustic Modelling for Large Vocabulary Continuous Speech Recognition. Computational Models of Speech Pattern Processing: Proc NATO Advance Study Institute. K. Ponting, Springer-Verlag: 18-38
 Rabiner, L. Juang, B. H., Yegnanarayana, B., “Fundamentals of Speech Recognition”, Pearson Publishers, 2010.
 ] L.,R Rabiner, "A tutorial on Hidden Markov model and selected application in speech recognition" , Pro.IEEE,7(2):257-286, February 1998
 Hwang, M. Y. (1993). Sub-phonetic acoustic modeling for speaker-independentcontinuous speech recognition. Ph.D. Thesis. Carnegie Mellon University.
 S. Young (1996). "Large Vocabulary Continuous Speech Recognition." IEEE Signal Processing Magazine 13(5): 45-57
 L.,R Rabiner, "A tutorial on Hidden Markov model and selected application in speech recognition" , Pro.IEEE,7(2):257-286, February 1998
Malayalam , Automatic Speech Recognition.