Dipanjan Nandi

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In this paper, we are introducing speech database consists of 27 Indian languages for analyzing language specific information present in speech. In the context of Indian languages, systematic analysis of various speech features and classification models in view of automatic language identification has not performed, because of the lack of proper speech(More)
The present work explores the significance of the consonant-vowel (CV) transition and steady vowel (SV) regions for language identification (LID) task. The language-specific vocal tract information represented by Mel-frequency cepstral coefficients (MFCCs), extracted from the CV transition and steady vowel regions for LID task. The duration of CV transition(More)
In this work, excitation source information is explored for language identification (LID) task. The excitation signal is represented by linear prediction (LP) residual. Different aspects of the excitation source information can be captured by processing LP residual signal at sub-segmental, segmental and supra-segmental levels. Gaussian mixture modelling(More)
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