Comparison Of Different Feature Extraction Techniques In Telugu Dialects Identification

Abstract

The Telugu language is one of the standard and historical languages in India. Like other spoken languages, the Telugu language also contains different dialects i.e., Telangana, Costa Andhra and Rayalaseema. The research in dialects identification (DI) work is very less as compared to other speech processing applications. For the research Dialect identification (DI) in Telugu language, database is very important, but there is no standard database. To carry out the research work in DI, we created the database of Telugu Language with different dialects and we anlyzed different feature extraction techniques to implement system. In this paper, we compare the performance of different models given by applying the different feature extraction techniques such as spatial, temporal, and prosodic features in Telugu dialect identification. We have applied different classification models i.e., K-Nearest Neighbour (K-NN), Gaussian Mixture Model (GMM) and Hidden Markov Model (HMM). It is observed that GMM model provides good accuracy with MFCC+∆MFCC+∆∆MFCC and MFCC+PITCH +LOUDNESS features compare to HMM model.