A Pre-classification-Based Language Identification for Northeast Indian Languages Using Prosody and Spectral Features

This paper is aimed at developing a two-stage language identification (LID) system for Northeast Indian languages. In the first stage, languages are pre-classified into tonal and non-tonal categories, and in the second stage, individual languages are identified from languages of the corresponding category. In this work, new parameters to model the prosodic characteristics of the speech signal have been proposed for pre-classification as well as individual language identification. Also, the effectiveness of spectral features, namely Mel-frequency cepstral coefficient (MFCC) and their combination with prosodic features, has been studied for pre-classification task. The usefulness of MFCC with their delta and acceleration coefficients in combination with prosodic features has been investigated for individual language identification. The performance of the system is analyzed for the features extracted of different analysis units, such as syllable, disyllable, word, and utterance. Comparative performance analysis of three different classifiers, namely artificial neural network (ANN), Gaussian mixture model-Universal background model (GMM-UBM), and i-vector based support vector machine (i-vector based SVM), has been made for pre-classification as well as individual language identification. A new database, NIT Silchar language database (NITS-LD), has been developed for seven NE Indian languages using All India Radio broadcast news. The experimental analysis suggests that the parameters proposed to represent the prosodic characteristics help to improve the performance of both the stages and show improvements over existing parameters by as much as 7.4%, 11.9%, and 9.1% for 30 s, 10 s, and 3 s test data, respectively, in the pre-classification stage. Of the baseline single-stage systems, GMM-UBM provides the highest accuracies of 80%, 76.8%, and 72% for 30 s, 10 s, and 3 s test data, respectively. In the proposed system, the combination of the ANN model in pre-classification stage and the GMM-UBM model in individual language identification stage provides the highest accuracies, and it shows the improvements over the baseline system by 7.2%, 7%, and 4.9% for 30 s, 10 s, and 3 s test data. For OGI-Multilingual (OGI-MLTS) database, improvements of 8.1%, 7.4%, and 5.7% for 30 s, 10 s, and 3 s test data, respectively, are observed over the baseline LID system.