Speech Classification for Kannada Language

Abstract

Speech classification is one of the challenging issues in speech processing. In this paper, we have done speech classification for the Kannada language. We have gathered a speech database from children aged 4-6 years. The dataset collected are pre-processed and speech feature extraction is done using Mel Frequency Cepstral Coefficients (MFCC) technique. After feature extraction Kannada alphabets are classified using six different Machine Learning (ML) classifiers. The classifier accuracies are compared with each other. Amongst the Deep Learning classifiers, Recursive Neural Network (RNN) gave the highest accuracy of around 93.6 %( for 300 epochs) and Random Forest (RF) gave the highest accuracy of around 88.9% which is a Machine Learning classifier.