Comparative analysis on the use of features and models for validating language identification system

Abstract

Identifying the spoken language from the speech is the emerging research area. For this task of language identification, experiments are implemented with two approaches such as Vector quantization (VQ) based clustering and Gaussian mixture modelling (GMM) with Mel frequency linear predictive cepstrum (MFPLPC), Mel frequency cepstrum (MFCC) and their shifted delta cepstral (SDC) features. Hypothesized language is identified based on minimum of averages and maximum log likelihood value corresponding to the model using minimum distance and Maximum a posteriori probability (MAP) classifiers. Better performance is observed for the basic feature MFPLP and VQ based clustering. The results are projected to indicate that the combined MFCC feature with its SDC component with size 52 has provided the better results using GMM as a modeling technique. Similarly, the combined MFPLP feature with its SDC component of size 52 provides next higher results as compared to the basic MFPLP feature of size 13 using clustering as a modeling technique. Overall performance of the system obtained is 99.81%. The database considered in this work contains speech utterances in seven classical and phonetically rich speaker specific Indian languages such as Bengali, Hindi, Kannada, Malayalam, Marathi, Tamil and Telugu.