Dynamic Time Warping Assisted SVM Classifier for Bangla Speech Recognition
Md. Masudur Rahman, Debopriya Roy Dipta, Md. Mahbub Hasan · 2018 International Conference on Computer, Communication, Chemical, Material and Electronic Engineering (IC4ME2) · 2018
In this paper, an automatic speech recognition system has been proposed for isolated Bangla word using Support vector machine with Dynamic time warping (DTW). For training purposes, we have collected data from 40 speakers for five different Bangla words. All the data was collected in a highly acoustic and noise-proof environment. Mel frequency cepstrum coefficients (MFCC’s) are considered as static features from the speech signal. For dynamic features, first and second derivatives of MFCC are utilized. After determining feature vectors, a modified DTW method is proposed for feature matching. Finally, for classification Support Vector Machine (SVM) with Radial basis function (RBF) is utilized. The model is tested for 12 speakers and the recognition rate that we achieved is 86.08%.