Title :
Spoken Arabic Digits recognition using MFCC based on GMM
Author :
Hammami, N. ; Bedda, M. ; Farah, Nadir
Author_Institution :
Lab. LabGed, Univ. Badji Mokhtar Annaba, Annaba, Algeria
Abstract :
Gaussian mixture model (GMM) is a conventional method for speech recognition, known for its effectiveness and scalability in speech modeling. This paper presents automatic recognition of the Spoken Arabic Digits based on (GMM) classifier and the leading approach for speech recognition features extraction Delta-Delta Mel- frequency cepstral coefficients (DDMFCC). The experimental results give the best result with the obtained parameters; they achieve a 99.31% correct digit recognition dataset which is very satisfactory compared to previous work on spoken Arabic digits speech recognition.
Keywords :
Gaussian processes; natural language processing; speech recognition; DDMFCC; GMM; Gaussian mixture model; MFCC; delta-delta mel-frequency cepstral coefficients; speech modeling; speech recognition features extraction; spoken Arabic digits speech recognition; Hidden Markov models; Mel frequency cepstral coefficient; Arabic speech recognition; Arabic spoken digits; DDMFCC; Gaussian mixture model (GMM); MFCC;
Conference_Titel :
Sustainable Utilization and Development in Engineering and Technology (STUDENT), 2012 IEEE Conference on
Conference_Location :
Kuala Lumpur
Print_ISBN :
978-1-4673-1649-1
Electronic_ISBN :
1985-5753
DOI :
10.1109/STUDENT.2012.6408392