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Research Article | Open Access |

Semi-Automatic Segmentation System for Syllables Extraction from Continuous Arabic Audio Signal

Author 1: Mohamed S. Abdo Author 2: Ahmed H. Kandil
International Journal of Advanced Computer Science and Applications (IJACSA) · Vol. 7, No. 1 · Published 2016 · Cited by 7

DOI: https://doi.org/10.14569/IJACSA.2016.070173

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Abstract

The paper describes a speaker independent segmentation system for breaking Arabic uttered sentences into its constituent syllables. The goal is to construct a database of acoustical Arabic syllables as a step towards a syllable-based Arabic speech verification/recognition system. The proposed technique segments the utterances based on maxima extraction from delta function of 1st MFC coefficient. This method locates syllables boundaries by applying the template matching technique with reference utterances. The system was applied over a data set of 276 utterances to segment them into their 2544 constituent syllables. A segmentation success rate of about 91.5% was reached.

Keywords

Arabic speech syllables automatic segmentation boundaries detection delta-MFCC features

How to Cite this Article

Abdo, M. S., & Kandil, A. H. (2016). Semi-Automatic Segmentation System for Syllables Extraction from Continuous Arabic Audio Signal. International Journal of Advanced Computer Science and Applications, 7(1). https://doi.org/10.14569/IJACSA.2016.070173

Abdo, Mohamed S., and Ahmed H. Kandil. "Semi-Automatic Segmentation System for Syllables Extraction from Continuous Arabic Audio Signal." International Journal of Advanced Computer Science and Applications, vol. 7, no. 1, 2016, https://doi.org/10.14569/IJACSA.2016.070173.

@article{Abdo2016,
  title     = {Semi-Automatic Segmentation System for Syllables Extraction from Continuous Arabic Audio Signal},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {7},
  number    = {1},
  year      = {2016},
  publisher = {The Science and Information Organization},
  author    = {Mohamed S. Abdo and Ahmed H. Kandil},
  doi       = {10.14569/IJACSA.2016.070173},
  url       = {https://doi.org/10.14569/IJACSA.2016.070173}
}

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