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2024
Vol. 1
no. 1
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Vol. 0
Vol. 1
Spoken Arabic Digits Classifier via Sophisticated Wavelet Transform Features Extraction Method
Pages
:
16-25
Khaled Daqrouq, Mikhled Alfaouri
The essential problem of Arabic recognition systems is the several of Arabic language dialects, especially along with associated noise. Therefore, low recognition rate is encountered, as a result of such an environment. In this research paper, the authors presented dialect-independent via sophisticated wavelet transform-based Arabic digits classifiers (SWADC). The proposed classifier is divided into three main blocks: 1) Filtration and widowing. 2) Sophisticated Features Extraction Method by combining Continuous Wavelet Transform (CWT) with Linear Prediction Coefficient (LPC) and Mel Frequency Cepstral Coefficient (MFCC). 3) Classification by Root Mean Square Difference Similarity Measure (RDSM) and Feed Forward Back Propagation Neural Network Classification (FFBPNC). The proposed classifier provided a high Recognition Rate reaches up to 100%, in some cases, and an average cases up to 95.9%, for about 450 tested individual digits, based on speaker-independent system.
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