Sparse Coding for Arabic Phoneme Classification

Sparse Coding for Arabic Phoneme Classification

  IJETT-book-cover  International Journal of Engineering Trends and Technology (IJETT)          
  
© 2017 by IJETT Journal
Volume-54 Number-1
Year of Publication : 2017
Authors : Dima Shaheen, Oumayma Al-Dakkak, MohieldinWianakh
DOI :  10.14445/22315381/IJETT-V54P204

Citation 

Dima Shaheen, Oumayma Al-Dakkak, MohieldinWianakh "Sparse Coding for Arabic Phoneme Classification", International Journal of Engineering Trends and Technology (IJETT), V54(1),17-27 December 2017. ISSN:2231-5381. www.ijettjournal.org. published by seventh sense research group

Abstract
Sparse Coding has been an active research topic in machine learning and signal processing for the last ten years, as it has achieved impressive results when applied to many problems such as face recognition and image denoising. In this paper, we present a new contribution in applying sparse coding to the problem of Arabic phoneme classification. The classification system which is entitled: Sparse Coding based phoneme Classification system (SCPCS), employs the sparse code as a new speech feature for classification using Sparse Representation Classifier. The Sparse code is simply the “coefficients” of the “sparse” (with many zeros) linear combination of basic signals that can represent the targeted signal as close as possible. We study the impact of the sparse coding solver which aims to produce the sparse code, on its discrimination capability. Experiments to evaluate the proposed system performance were conducted on two manually segmented Arabic phonemes, extracted from KAPD (King Abdulaziz city for science and technology Arabic Phonetic Database) and CSLU2002 (Centre for Spoken Language Understanding) Arabic speech databases. Experimental results showed that the proposed system has achieved an accuracy of 85.3% on KAPD and 53.4% on CSLU2002, which are better than state of the art results in these two datasets.

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Keywords
Compressive Sensing, Sparse Coding, phoneme classification, dictionary learning, Sparse Representation Classifier SRC, l1 minimization algorithms, cross-validation.