A comprehensive approach for performance evaluation of Indian language identification systems

Abstract

Good feature representation is the chief requirement for improving Language Identification (LID) system recognition performance. In this work LID system for Indian languages is proposed based on unsupervised feature learning utilizing Deep Belief Network (DBN). The proposed methodology is implemented in two parts. The first phase of this work is based on extracting MFCC features combined with SDC hybrid features. The resultant hybrid features are further stacked to Deep Belief Network (DBN). The second phase of the proposed work is investigating the performance of various Feed forward back propagation neural network models for classification using different training algorithms. Effect of combining different activation functions and varying the hidden neurons is also investigated The performance of the resultant models is evaluated on the basis of some performance metrics such as the epochs, training time, Mean Square Error, Regression and Mean Absolute Percentage Error. Results indicate that optimal performance is achieved in model trained with Levenberg Marquardt (LM) training algorithm. The activation functions used in the hidden and output layer are “tansig” and “purelin”. Similarly, the effect of varying the number of neurons in the hidden layer is not significant in improving the performance of the derived models. FFBPNN models trained with PL and TS activation functions gave best performance indices. A user defined language database in four different languages Hindi, English, Tamil and Malayalam is used for this work.