Language Identification Performance Evaluation Using Spectral Processing

Language Identification (LID) is a method by which the language is identified from an utterance communicated by an unidentified person. Present work develops an effective baseline method with the Gaussian Mixture Model (GMM) and Mel Frequency Cepstral Coefficients (MFCC) for language identification and the performance of the LID system is evaluated for an unknown speaker. The LID framework is designed utilizing a user-defined database in 4 different languages Tamil, Malayalam, Hindi, and English. This work is based on some optimization approaches such as Minimum Mean Square Error (MMSE) and Spectral Subtraction (SS) to improve the LID performance with background noise. Moreover, the LID system performance is also investigated by changing the size of data used to train the system, duration of test data as well as the amount of noise.