Effect of Time Derivatives of MFCC Features on HMM Based Speech Recognition System

In this paper, improvement of an ASR system for Hindi language, based on Vector quantized MFCC as feature vectors and HMM as classifier, is discussed. MFCC features are usually pre-processed before being used for recognition. One of these pre-processing is to create delta and delta-delta coefficients and append them to MFCC to create feature vector. This paper focuses on all digits in Hindi (Zero to Nine), which is based on isolated word structure. Performance of the system is evaluated by accurate Recognition Rate (RR). The effect of the combination of the Delta MFCC (DMFCC) feature along with the Delta-Delta MFCC (DDMFCC) feature shows approximately 2.5% further improvement in the RR, with no additional computational costs involved. RR of the system for the speakers involved in the training phase is found to give better recognition accuracy than that for the speakers who were not involved in the training phase. Word wise RR is observed to be good in some digits with distinct phones.