Bangla Phoneme Recognition for Different Acoustic Features

Abstract

In this paper, we compare among performance of different acoustic features for Bangla Automatic Speech Recognition (ASR). Most of the Bangla ASR system uses a small number of speakers, but 40 speakers selected from a wide area of Bangladesh, where Bangla is used as a native language, are involved here. In the experiments, mel-frequency cepstral coefficients (MFCCs) and local features (LFs) are inputted to the hidden Markov model (HMM) based classifiers for obtaining phoneme recognition performance. It is shown from the experimental results that MFCC-based method of 39 dimensions provides a higher phoneme correct rate and accuracy than the other methods investigated.