Classification and clustering to identify spoken dialects in Indonesian

Abstract

This paper explains classification using Support Vector Machines (SVM) technique and clustering using K-means technique in identifying eight spoken dialects in Indonesian language. Dialect identification is important to build a better Automatic Speech Recognition system. The experiment in this research is divided into using three features of sound; Mel Frequency Cepstral Coefficient (MFCC), spectral flux, and spectral centroid, and compares it to model with MFCC features only. For methods, it uses one-against-one and all-at-once as comparison. The best result is from using SVM one-against-one with three features which gives 55%.