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Automatic Speech Recognition of Alveolar Rhotic and Retroflex Rhotic Phonemes of Malayalam Language

Cini Kurian1

Section:Research Paper, Product Type: Journal Paper
Volume-7 , Issue-5 , Page no. 1283-1286, May-2019

CrossRef-DOI:   https://doi.org/10.26438/ijcse/v7i5.12831286

Online published on May 31, 2019

Copyright © Cini Kurian . This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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IEEE Style Citation: Cini Kurian, “Automatic Speech Recognition of Alveolar Rhotic and Retroflex Rhotic Phonemes of Malayalam Language,” International Journal of Computer Sciences and Engineering, Vol.7, Issue.5, pp.1283-1286, 2019.

MLA Style Citation: Cini Kurian "Automatic Speech Recognition of Alveolar Rhotic and Retroflex Rhotic Phonemes of Malayalam Language." International Journal of Computer Sciences and Engineering 7.5 (2019): 1283-1286.

APA Style Citation: Cini Kurian, (2019). Automatic Speech Recognition of Alveolar Rhotic and Retroflex Rhotic Phonemes of Malayalam Language. International Journal of Computer Sciences and Engineering, 7(5), 1283-1286.

BibTex Style Citation:
@article{Kurian_2019,
author = {Cini Kurian},
title = {Automatic Speech Recognition of Alveolar Rhotic and Retroflex Rhotic Phonemes of Malayalam Language},
journal = {International Journal of Computer Sciences and Engineering},
issue_date = {5 2019},
volume = {7},
Issue = {5},
month = {5},
year = {2019},
issn = {2347-2693},
pages = {1283-1286},
url = {https://www.ijcseonline.org/full_paper_view.php?paper_id=4403},
doi = {https://doi.org/10.26438/ijcse/v7i5.12831286}
publisher = {IJCSE, Indore, INDIA},
}

RIS Style Citation:
TY - JOUR
DO = {https://doi.org/10.26438/ijcse/v7i5.12831286}
UR - https://www.ijcseonline.org/full_paper_view.php?paper_id=4403
TI - Automatic Speech Recognition of Alveolar Rhotic and Retroflex Rhotic Phonemes of Malayalam Language
T2 - International Journal of Computer Sciences and Engineering
AU - Cini Kurian
PY - 2019
DA - 2019/05/31
PB - IJCSE, Indore, INDIA
SP - 1283-1286
IS - 5
VL - 7
SN - 2347-2693
ER -

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Abstract

Development of speech recognition systems in local languages will help anyone to make use of the technological advancement of the speech recognition . In India, speech recognition systems have been developed for many indigenous languages, however very less work has been done in Malayalam Language. Malayalam language is famous for its unique phonemes. Hence one of the main objectives of this work is to explore the Alveolar and Retroflex phonemes of Malayalam language which has unique phonetic realizations.

Key-Words / Index Term

Automatic Speech Recognition , Malayalam , Phonome

References

[1] Sorin Dusan and Larry R. Rabiner, “On integrating insights from human speech perception into automatic speech recognition,” in Proceedings of INTERSPEECH 2005, Lisbon, 2005.
[2] HILL, D. R. (1971). Man-machine interaction using speech. In Advances in Computers, 11. Eds F. L. Alt, M. Rubinoff & M. C. Yovitts, pp. 165-230. New York: Academic Press.
[3] 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.
[4] G. Doddington, (1989), "Phonetically Sensitive Discriminants for Improved Speech Rec.", Proc. IEEE Int Conf. Acoustics. Speech and Sig. Proc., ICASSP-89, pp. 556-559, Glasgow, Scot- land.
[5]Itakura F (1975) Minimum prediction residual principle applied to speech recognition. IEEE Trans Acoustics Speech Signal Process ASSP 23:52–72
[6] Miyatake, M. Sawai, H., & Shikano, K. (1990). Integrated Training for Spotting Japanese Phonemes Using Large Phonemic Time-Delay Neural Networks. In Proc. IEEE International Conference on Acoustics, Speech, and Signal Processing, 1990.
[7] Kimura, S. (1990). 100,000-Word Recognition Using Acoustic-Segment Networks. In Proc.IEEE International Conference on Acoustics, Speech, and Signal Processing.
[8]K.-F. Lee, Large-vocabulary speaker-independent continuous speech recognition: The Sphinx system, Ph.D. Thesis, Carnegie Mellon University, 1988.
[9] 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.
[10] Bahl, L. R. et al. (1978). Automatic Recognition of Continuously Spoken Sentences from a Finite State Grammar. In Proc ICASSP, pp. 418-421.
[11] Young, S., Evermann, G., Kershaw, D., Moore, G., Odell, J., Ollason, D., Povey, D.,Valtchev, V. and Woodland, P. (2002). The HTK Book (for HTK Version 3.2).Microsoft Corporation and Cambridge University Engineering Department,England.
[12] Young (1996). "Large Vocabulary Continuous Speech Recognition." IEEE Signal Processing Magazine 13(5): 45-57
13] Punnoose, R. (2010). An Auditory and Acoustic Study of Liquids in Malayalam. Ph.D. Thesis, Newcastle University, Newcastle, UK
[14] J. Holmes (1988). Speech synthesis and recognition. Van Nostrand Reinhold (UK) Co. Ltd., Wokingham.