Abstract
Language Identification is a system of recognizing the dialect from a speech sample articulated by an obscure speaker. In this paper, we tend to develop an efficient baseline system for language identification by Mel Frequency Cepstral Coefficients (MFCC) and Gaussian Mixture Model (GMM). The Language Identification (LID) performance is analyzed in speaker independent approach. LID system is developed using IITKGP-MLILSC database. To enhance the LID performance under background noise we use some enhancement techniques like Spectral Subtraction (SS) and Minimum Mean Square Error (MMSE). Here, we present the performance of LID system by varying the amount of training data, the length of testing samples and the background noise.
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