GMM Vector Quantization on the Modeling of DHMM for Arabic Isolated Word Recognition System
Snani Cherifa, Ramdani Messaoud, Zermi Narima, Bourouba Houcine
International Journal of Computational Linguistics Research, Vol. 4, No. 3, 2013.
DOI: 
Link: http://www.dline.info/jcl/fulltext/v4n3/2.pdf
Abstract:
Vector quantization based on a codebook is a basic process to recognition the speech signal by discrete hidden markov model. This article identifies the fundamental importance of vector quantization codebooks in the performance of the system. For comparison, two different algorithms k-means and Gaussian mixture models (GMM) have been used to obtain two sets of speech feature codebook. We used in analysis phase Mel Frequency Cepstral Coefficients (MFCC) supplement by dynamic features to increase system performance, although experiments are carried out for the choice of the optimal parameters of the system. Good results are obtained using a GMM method.