Open AccessSpeech Classification for Kannada Language
Author(s) -
Supriya B. Rao,
Sarika Hegde
Publication year - 2020
Publication title -
international journal of innovative technology and exploring engineering
Language(s) - English
Resource type - Journals
ISSN - 2278-3075
DOI - 10.35940/ijitee.e2969.039520
Subject(s) - kannada , mel frequency cepstrum , computer science , classifier (uml) , random forest , speech recognition , artificial intelligence , feature extraction , support vector machine , artificial neural network , pattern recognition (psychology) , natural language processing
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.