School of Computer Science and Software Engineering, University of Wollongong, Australia. softwarist@yahoo.com
Online published on 8 November, 2017.
This paper presents a technique for recognizing spoken letter in Bengali Language. We first derive feature from spoken letter. Mel-frequency cepstral coefficient (MFCC) has been used to characterize a feature. Dynamic time warping (DTW) employed to calculate the distance of an unknown letter with the stored ones. K-nearest neighbors (KNN) algorithm is used to improve accuracy in noisy environment.
Automatic Speech Recognition (ASR), MFCC, DTW, KNN, Bengali Alphabets
