Abstract
Language Identification has gained significant importance in recent years, both in research and commercial market place, demanding an improvement in the ability of machines to distinguish languages. Although methods like Gaussian Mixture Models, Hidden Markov Models and Neural Networks are used for identifying languages the problem of language identification in noisy environments could not be addressed so far. This paper addresses the capability of an Automatic Language Identification (LID) system in clean and noisy environments. The language identification studies are performed using IITKGP-MLILSC (IIT Kharagpur-Multilingual Indian Language Speech Corpus) databases which consists of 27 languages.
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Acknowledgments
The authors are grateful to Dr K Sreenivasa Rao, Associate Professor and his team at School of Information Technology (SIT), IIT Kharagpur for providing IIT Kharagpur-Multilingual Indian Language Speech Corpus) databases which consists of 27 languages. We would also like to thank their anonymous suggestions and helpful discussions.
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Polasi, P.K., Sri Rama Krishna, K. (2016). Performance of Speaker Independent Language Identification System Under Various Noise Environments. In: Satapathy, S., Mandal, J., Udgata, S., Bhateja, V. (eds) Information Systems Design and Intelligent Applications. Advances in Intelligent Systems and Computing, vol 433. Springer, New Delhi. https://doi.org/10.1007/978-81-322-2755-7_33
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DOI: https://doi.org/10.1007/978-81-322-2755-7_33
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