Title :
Performance study of vector quantization methods (k-means, GMM) for arabic isolated word recognition system based on DHMM
Author :
Cherifa, S. ; Messaoud, R. ; Narima, Zermi ; Houcine, Bourouba
Author_Institution :
Lab. d´Autom. et Signaux d´Annaba (LASA), Univ. Badji Mokhtar de Annaba, Annaba, Algeria
Abstract :
This article present several techniques used in the design of an isolated Arabic words recognition system based on the discrete Hidden Markov Model. We present the results of an experimental study aimed at finding the effect of the quantization methods (k-means and GMM) on the recognition performance. we used in analysis phase Mel Frequency Cepstral Coefficients (MFCC), although experiments are carried out for the choice of the optimal parameters of the system. Good results are obtained using a GMM method.
Keywords :
Gaussian processes; hidden Markov models; natural language processing; pattern clustering; vector quantisation; word processing; Arabic isolated word recognition system; DHMM; GMM method; MFCC; Mel frequency cepstral coefficients; discrete hidden Markov model; k-means method; vector quantization; Computational modeling; Hidden Markov models; Mel frequency cepstral coefficient; Speech; Speech recognition; Support vector machine classification; Vectors; Gaussian mixture models (GMM); Mel Frequency Cepstral Coefficients (MFC); discret hidden markov model(DHMM); k-means; speech recognition system (SRS);
Conference_Titel :
Computer and Information Technology (WCCIT), 2013 World Congress on
Conference_Location :
Sousse
Print_ISBN :
978-1-4799-0460-0
DOI :
10.1109/WCCIT.2013.6618722
