Fourier-Bessel cepstral coefficients for robust speech recognition

In this paper we propose Fourier-Bessel cepstral coefficients (FBCC) features for robust speech recognition. The Fourier-Bessel representation of the speech signal is obtained using Bessel function as a basis set. The FBCC are extracted from zero^th order Bessel coefficients taking into account of the perceptual characteristics of human auditory system. Recognition accuracy is measured using the CMU SPHINX-III speech recognition system using the DARPA Resource Management (RM) speech corpus for training and testing. We evaluate the FBCC in a common experimental set up and compare their performance against traditional technique such as the Mel-frequency cepstral coefficients (MFCC) for various noise conditions. The recognition accuracy is found to be better using FBCC features in comparison with MFCC features under noisy condition data.