Comparative Study of Different Classifiers for Malayalam Dialect Recognition System

AbstractMalayalam is a South Indian language spoken predominantly in the state of Kerala. In this paper, a comparative study of different classifiers to recognize Malayalam language dialects has been carried out and presented. Thrissur and Kozhikode are the two different dialect corpora used for the recognition task. Mel Frequency Cepstral Coefficients (MFCC), energy and pitch are the features extracted. Then these feature vector set obtained are classified in the classification phase using Artificial Neural Networks (ANN), Support Vector Machine (SVM) and Naive Bayes classifiers. The input feature vector data is trained using data relating to patterns which are known and using the test data set they are tested further. Based on recognition accuracy, the performance of the ANN, SVM, and Naive Bayes are evaluated. Speech recognition is a multiclass classification problem. During classification stage, the input feature vector data is trained using information relating to known patterns and then they are tested using the test data set. ANN produced a recognition accuracy of 90.2%, SVM produced an accuracy of 88.2% and Naive Bayes produced an accuracy of 84.1%. Among the three classifiers, ANN is found to be better