article

Comparison of performance of enhanced morpheme-based language model with different word-based language models for improving the performance of Tamil speech recognition system

Authors:

T. V. GeethaAuthors Info & Claims

ACM Transactions on Asian Language Information Processing (TALIP), Volume 6, Issue 3

Pages 9 - es

https://doi.org/10.1145/1290002.1290003

Published: 01 November 2007 Publication History

Abstract

This paper describes a new technique of language modeling for a highly inflectional Dravidian language, Tamil. It aims to alleviate the main problems encountered in processing of Tamil language, like enormous vocabulary growth caused by the large number of different forms derived from one word. The size of the vocabulary was reduced by, decomposing the words into stems and endings and storing these sub word units (morphemes) in the vocabulary separately. A enhanced morpheme-based language model was designed for the inflectional language Tamil. The enhanced morpheme-based language model was trained on the decomposed corpus. The perplexity and Word Error Rate (WER) were obtained to check the efficiency of the model for Tamil speech recognition system. The results were compared with word-based bigram and trigram language models, distance based language model, dependency based language model and class based language model. From the results it was analyzed that the enhanced morpheme-based trigram model with Katz back-off smoothing effect improved the performance of the Tamil speech recognition system when compared to the word-based language models.

References

[1]

Ali, A.M.A. 1998. Segmentation and Categorization of Phonemes in Continuous Speech. Technical Report TRCST25JUL98, Center for Sensor Technology, University of Pennsylvania.

[2]

Ali, A.M.A., Vander Spiegel, J., Mueller, P., Haentjens, G., and Berman, J. 1999. An Acoustic-Phonetic feature based system for Automatic Phoneme Recognition in Continuous Speech. In IEEE Proceedings of the International Symposium on Circuits and Systems (ISCAS), 3, 118--121.

[3]

ALI, A.M.A. 2001. Acoustic--Phonetic Features for the Automatic Classification of Stop Consonants. IEEE Trans. Speech and Audio Processing, 9(8), 833--841.

[4]

Anandan, P., Geetha, T.V., and Paratasarathy, R. 2001. Morphological Generator for Tamil. In Proceedings of the Tamil Inayam Conference, Malaysia, 46--54.

[5]

Anandan, P., Saravanan, K., Partasarathi, R., and Geetha, T.V. 2002. Morphological Analyzer for Tamil. In Proceedings of ICON2002, 3--10.

[6]

Arden, A.H. Rev. and Clayton, A. C. 1969. A Progressive Grammar of the Tamil Language, Christian Literature Society, Madras.

[7]

Aversano, G., Esposito, A., Esposito, A., and Marinaro, M. 2001. A new Text Independent Method for Phoneme Segmentation. In Proceedings of IEEE International Workshop on Circuits and Systems, 2, 516--519.

[8]

Aversano, G., Esposito, A., and Chollet, G., 2003. A JAVA interface for speech analysis and segmentation. In Proceedings of the ISCA tutorial and Research Workshop on Non-linear Speech Processing (NOLISP-03), Le Croisic, France, Paper. 026.

[9]

Brown, P., Pietra, S., Della Pietra, V., and Mercer, R. 1993. The mathematics of statistical machine translation: Parameter estimation', Computational Linguistics, 19(2), 263--311.

[10]

Byrne, W., Hacic, J., Iircing, P., Jelinek, F., Khudanpur, S., Krbec,.P., and Psutka, J. 2001. On large vocabulary continuous speech recognition of highly inflectional language -- Czech. In Proceedings of Eurospeech 2001, Aalborg, Denmark, 487--489.

[11]

Choudhury, M. 2003. Rule based grapheme to phoneme mapping for Hindi speech synthesis, presented in 90th Indian Science Congress of ISCA, Bangalore (http://www.mla.iitkgp.ernet.in/papers/G2Phindi.pdf. )

[12]

Collin, M. J. 1996. A new statistical parser based on lexical dependencies. In Proceedings of the 34th Annual Meeting of the Association for Computational Linguistics, 184--191.

[13]

Creutz, M., and Lagus, K. 2002. Unsupervised discovery of Morphemes. In Proceedings of workshop on Morphological and Phonological Learning of ACL 2002, 21--30.

[14]

Franz, M., and Mccarley, S. 2002. Arabic information retrieval at IBM, In Proceedings of TREC 2002, 402--405.

[15]

Gao, J., and Suzuki, H. 2003. Unsupervised learning of dependency structure for language modeling. In Proceedings of ACL-2003, 7--12.

[16]

Huckvale, M., and Fang, A. C. 2002. Using phonologically constrained Morphological Analysis in Speech Recognition. Computer Speech and Language, 165-181.

[17]

Kneissler, J., and Klakow, D. 2001. Speech recognition for huge vocabularies by using optimized sub-word units. In Proceedings of Eurospeech 2001, 69--72.

[18]

Lafferty, J., Sleator, D., and Temperley, D., 1992. Grammatical Trigrams: A probabilistic model of link grammars, In Proceedings of AAAI Fall Symposium on Probabilistic Approaches to Natural LanguagI Issued as technical report CMU-CS-92-181.

[19]

Lafferty, S. and Suhm, B. 1995. Cluster Expansion and Iterative scaling of Maximum Entropy Language Models. In Hanson, K., Silver, R. (Eds) Maximum Entropy and Bayesian Methods, Kluwer Academic Publishers, Norwell, MA.

[20]

Roark B. 2001. Probabilistic top-down parsing and language modeling. Computational Linguistics, 27(2), 249--285.

[21]

Saraswathi, S., and Geetha, T.V. 2004. Building language models for Tamil speech recognition system. In Proceedings of AACC2004 and LNCS3285, 161--168.

[22]

Saraswathi, S., Geetha T.V., and Saravanan, K. 2006a. Integrating language independent segmentation and language dependent phoneme based modeling for Tamil Speech Recognition. Asian J. Information Technology, 5(1), 38--43.

[23]

Saraswathi S., Rajeswari, S., and Geetha T.V. 2006b. Tamil Phoneme segmentation by combining spectral and temporal features. In Proceedingsof the Frontiers of Research on Speech and Music (FRSM-2006). 54--57.

[24]

Smaili, K., Brun A., Zitouni, I., and Haton, J.P. 1999. Automatic and manual clustering for large vocabulary speech recognition - A comparative study. In Proceedings of 6th European Conference on Speech Communication and Technology, 1795--1798.

[25]

Souto, N., Meinedo, H., and Neto, J. 2002. Building Language Models for Continuous Speech Recognition Systems. In Proceedings of Portugal for Natural Language Processing (PorTAL), 101--110.

[26]

Sun, J., Gao, J., Zhang, L., Zhou, M., and Huang, C. 2002. Chinese named entity identification using class based language model, In Proceedings of the 19th International Conf. on Computational Linguistics, 1, 1--7.

[27]

Siivola, V., Hirsimaki, T., Creutz, M., and Kurimo, M., 2003. Unlimited Vocabulary speech recognition based on morphs discovered in an unsupervised manner. In Proc. Eurospeech 2003, 2293--2296.

[28]

Wu, J., and Zheng, F. 2000. On Enhancing Katz smoothing Based on Back-off Language Model. In Proceedings of the International Conference on Speech and Language Processing (ICSLP'2000), 1, 198--201.

[29]

Xu, P., Chelba, C., and Jelinek, F. 2002. A study on richer syntactic dependencies for structured language modeling. In Proceedings of ACL,191--198.

[30]

Xuedong, H., Acero, A., and Hon, H.-W. 2001. Spoken Language Processing, Prentice Hall Publication.

Cited By

Singh AKaur NKukreja VKadyan VKumar M(2022)Computational intelligence in processing of speech acoustics: a surveyComplex & Intelligent Systems10.1007/s40747-022-00665-18:3(2623-2661)Online publication date: 17-Feb-2022
https://doi.org/10.1007/s40747-022-00665-1
Mzamo LHelberg ABosch S(2015)Introducing XGL - a lexicalised probabilistic graphical lemmatiser for isiXhosa2015 Pattern Recognition Association of South Africa and Robotics and Mechatronics International Conference (PRASA-RobMech)10.1109/RoboMech.2015.7359513(142-147)Online publication date: Nov-2015
https://doi.org/10.1109/RoboMech.2015.7359513
Aggarwal RDave M(2012)Integration of multiple acoustic and language models for improved Hindi speech recognition systemInternational Journal of Speech Technology10.1007/s10772-012-9131-y15:2(165-180)Online publication date: 1-Jun-2012
https://dl.acm.org/doi/10.1007/s10772-012-9131-y
Show More Cited By

Index Terms

Comparison of performance of enhanced morpheme-based language model with different word-based language models for improving the performance of Tamil speech recognition system
1. Computing methodologies
  1. Artificial intelligence
    1. Natural language processing

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Published In

cover image ACM Transactions on Asian Language Information Processing

ACM Transactions on Asian Language Information Processing Volume 6, Issue 3

November 2007

58 pages

ISSN:1530-0226

EISSN:1558-3430

DOI:10.1145/1290002

Issue’s Table of Contents

Copyright © 2007 ACM.

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 01 November 2007

Published in TALIP Volume 6, Issue 3

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Cited By

Singh AKaur NKukreja VKadyan VKumar M(2022)Computational intelligence in processing of speech acoustics: a surveyComplex & Intelligent Systems10.1007/s40747-022-00665-18:3(2623-2661)Online publication date: 17-Feb-2022
https://doi.org/10.1007/s40747-022-00665-1
Mzamo LHelberg ABosch S(2015)Introducing XGL - a lexicalised probabilistic graphical lemmatiser for isiXhosa2015 Pattern Recognition Association of South Africa and Robotics and Mechatronics International Conference (PRASA-RobMech)10.1109/RoboMech.2015.7359513(142-147)Online publication date: Nov-2015
https://doi.org/10.1109/RoboMech.2015.7359513
Aggarwal RDave M(2012)Integration of multiple acoustic and language models for improved Hindi speech recognition systemInternational Journal of Speech Technology10.1007/s10772-012-9131-y15:2(165-180)Online publication date: 1-Jun-2012
https://dl.acm.org/doi/10.1007/s10772-012-9131-y
Thangarajan RNatarajan ASelvam M(2009)Syllable modeling in continuous speech recognition for Tamil languageInternational Journal of Speech Technology10.1007/s10772-009-9058-012:1(47-57)Online publication date: 18-Nov-2009
https://doi.org/10.1007/s10772-009-9058-0

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