Performance comparison of MFCC based bangla ASR system in presence and absence of third differential coefficients

Present Mel Frequency Cepstral Coefficient (MFCC) based Bangla Automatic Speech Recognition (ASR) systems are mostly implemented with delta and acceleration coefficients. With delta and acceleration coefficients of MFCC and the log energy, a vector set of 39 dimensions is obtained per 10ms. In this paper, our objective is to observe the effect of third differential coefficients on the performance of Bangla ASR, which is not explored in this field yet. In doing so, we have appended 13 third differential coefficients along with previous 39 coefficients to make a vector set of 52 coefficients per 10ms frame. We have observed the performance of Bangla ASR system in the presence and absence of third differential coefficients using Hidden Markov Model (HMM) based tied-state triphone model. To make the speech corpus, 100 sentences have been uttered by a different number of speakers at different phases including both male and female of similar ages in between 22–24. Hidden-Markov-Model Toolkit (HTK) has been used here for the comparative analysis. We have considered the Sentence Correction Rate (SCR) as the performance indicator. From the experiments, it has been observed that the MFCC based system of 39 (MFCC39) and 52 (MFCC52) dimensions have average SCR of 98.89% and 98.94% respectively. Therefore, our finding is that slight improvement is possible with the inclusion of third differential coefficients when the sampling data rate is as high as 44.1 KHz.