The second-order derivatives of MFCC for improving spoken Arabic digits recognition using Tree distributions approximation model and HMMs

Abstract

Mel Frequency Cepstral Coefficients (MFCCs) are the most popularly used speech features in many speech and speaker recognition applications. In this paper, we study the effect of the second-order derivatives of MFCC on the recognition of the Spoken Arabic digits. The system was developed using the Hidden Markov Models (HMMs) and Tree distribution approximation model. Experimentally it has been shown that, the second-order derivatives of MFCC parameters compared to the MFCC yield improved rates of 4.60% for CHMM. We were able to reach an overall recognition accuracy of 98.41%, which is satisfactory compared to previous work on spoken Arabic digits speech recognition.