Performance of a language identification system using hybrid features and ANN learning algorithms

Abstract

This paper discusses the use of hybrid features and artificial neural network (ANN) for spoken language identification (LID). At the feature extraction stage, we have used RASTA-PLP features and later hybrid features by combining the state-of-the-art MFCC features with RASTA-PLP features. The classifier used at the back-end of the LID system is Feed-forward Back Propagation Neural Network (FFBPNN). Performance comparison is done for all the thirteen learning algorithms available in FFBPNN. Results indicate better performance with MFCC + RASTA-PLP features as compared to RASTA-PLP features when used individually. The classification accuracy of 94.6 percent is obtained with ‘trainlm’ network training function and a test error rate of 0.10 with MFCC + RASTA-PLP hybrid features. On the other hand, RASTA-PLP features provide the best classification accuracy of 89.6%. Also, in this paper two new training functions are proposed. Experimental results show that for the MFCC + RASTA-PLP features, the proposed training function 1 offers 95.1 percent accuracy and 0.0993 mean square error. Similarly, the proposed training function 2 offers 95.3 per cent classification accuracy and 0.093 minimum mean square error for MFCC + RASTA-PLP features. The feasibility of the proposed solution is estimated by simulating multiple experiments on a language database of four languages i.e. Hindi, Tamil, Malayalam and English in the working Platform of MATLAB.