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Machine identification of the language of input speech is of practical interest in regions where people are either bilingual or multi-lingual. Here, we present the development of automatic language identification system that identifies the language of input speech as one of Assamese or Bengali or English spoken by them. The speech databases comprise of sentences read by multiple speakers using their mobile phones. Kaldi toolkit was used to train acoustic models based on hidden Markov model in conjunction with Gaussian mixture models and deep neural networks. The accuracy of the implemented language identification system for test data is 99.3%.

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