2015
DOI: 10.1007/s10772-015-9326-0
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Combining the evidences of temporal and spectral enhancement techniques for improving the performance of Indian language identification system in the presence of background noise

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 between 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 performance of automatic language identification system in noisy env… Show more

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Cited by 7 publications
(1 citation statement)
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“…Our study conclude that the techniques used for sentiment analysis from speech thus far, work better on a larger dataset and on single language. There is no historical evidence of emotion extraction from multilingual speech data of Indian languages [42][43][44][45][46][47][48][49][50][51][52][53][54][55][56][57]. Our research is based on 8 Indian languages: Hindi, Gujarati, Marathi, Punjabi, Bangla, Tamil, Oriya, and Telugu.…”
Section: Introductionmentioning
confidence: 99%
“…Our study conclude that the techniques used for sentiment analysis from speech thus far, work better on a larger dataset and on single language. There is no historical evidence of emotion extraction from multilingual speech data of Indian languages [42][43][44][45][46][47][48][49][50][51][52][53][54][55][56][57]. Our research is based on 8 Indian languages: Hindi, Gujarati, Marathi, Punjabi, Bangla, Tamil, Oriya, and Telugu.…”
Section: Introductionmentioning
confidence: 99%