Hybrid Techniques for Arabic Letter Recognition

Mohamed Hassine; Lotfi Boussaid; Hassani Massouad

doi:doi:10.11648/j.ijiis.20150401.14

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Hybrid Techniques for Arabic Letter Recognition

Mohamed Hassine, Lotfi Boussaid, Hassani Massouad

Published in International Journal of Intelligent Information Systems (Volume 4, Issue 1)

Received: 30 August 2014 Accepted: 22 January 2015 Published: 2 February 2015

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Abstract

In this paper we investigate the use of the feed-forward back propagation neural networks (FFBPNN) for automatic speech recognition of Arabic letters with their four vowels (Fatha, dhamma, Kasra, Soukoun). This investigation will constitute a basically step for the recognition of continuous Speech. Features were extracted from recorded corpus by using a variety of conventional methods such as Linear Predictive Codes (LPC), Perceptual Linear Prediction (PLP), Relative Spectral Perceptual Linear Prediction (RASTA-PLP), Mel Frequency Cepstral Coefficients (MFCC), Continuous Wavelet Transform (CWT), etc. Here, several hybrid methods have been used too. Since the extracted features have large dimensionalities they were reduced by conserving the most discriminatory information with the Principal Component Analysis (PCA) technique. The recognition performance has been improved particularly when we use the PLP method followed by PCA technique.

Published in	International Journal of Intelligent Information Systems (Volume 4, Issue 1)
DOI	10.11648/j.ijiis.20150401.14
Page(s)	27-34
Creative Commons	This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.
Copyright	Copyright © The Author(s), 2024. Published by Science Publishing Group

Keywords

Speech Recognition, Arabic Letters, Hybrid Techniques, MFCC, PLP, LPCC, PCA and FFBPNN

References

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APA Style

Mohamed Hassine, Lotfi Boussaid, Hassani Massouad. (2015). Hybrid Techniques for Arabic Letter Recognition. International Journal of Intelligent Information Systems, 4(1), 27-34. https://doi.org/10.11648/j.ijiis.20150401.14

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ACS Style

Mohamed Hassine; Lotfi Boussaid; Hassani Massouad. Hybrid Techniques for Arabic Letter Recognition. Int. J. Intell. Inf. Syst. 2015, 4(1), 27-34. doi: 10.11648/j.ijiis.20150401.14

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AMA Style

Mohamed Hassine, Lotfi Boussaid, Hassani Massouad. Hybrid Techniques for Arabic Letter Recognition. Int J Intell Inf Syst. 2015;4(1):27-34. doi: 10.11648/j.ijiis.20150401.14

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@article{10.11648/j.ijiis.20150401.14,
  author = {Mohamed Hassine and Lotfi Boussaid and Hassani Massouad},
  title = {Hybrid Techniques for Arabic Letter Recognition},
  journal = {International Journal of Intelligent Information Systems},
  volume = {4},
  number = {1},
  pages = {27-34},
  doi = {10.11648/j.ijiis.20150401.14},
  url = {https://doi.org/10.11648/j.ijiis.20150401.14},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ijiis.20150401.14},
  abstract = {In this paper we investigate the use of the feed-forward back propagation neural networks (FFBPNN) for automatic speech recognition of Arabic letters with their four vowels (Fatha, dhamma, Kasra, Soukoun). This investigation will constitute a basically step for the recognition of continuous Speech. Features were extracted from recorded corpus by using a variety of conventional methods such as Linear Predictive Codes (LPC), Perceptual Linear Prediction (PLP), Relative Spectral Perceptual Linear Prediction (RASTA-PLP), Mel Frequency Cepstral Coefficients (MFCC), Continuous Wavelet Transform (CWT), etc. Here, several hybrid methods have been used too. Since the extracted features have large dimensionalities they were reduced by conserving the most discriminatory information with the Principal Component Analysis (PCA) technique. The recognition performance has been improved particularly when we use the PLP method followed by PCA technique.},
 year = {2015}
}

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TY  - JOUR
T1  - Hybrid Techniques for Arabic Letter Recognition
AU  - Mohamed Hassine
AU  - Lotfi Boussaid
AU  - Hassani Massouad
Y1  - 2015/02/02
PY  - 2015
N1  - https://doi.org/10.11648/j.ijiis.20150401.14
DO  - 10.11648/j.ijiis.20150401.14
T2  - International Journal of Intelligent Information Systems
JF  - International Journal of Intelligent Information Systems
JO  - International Journal of Intelligent Information Systems
SP  - 27
EP  - 34
PB  - Science Publishing Group
SN  - 2328-7683
UR  - https://doi.org/10.11648/j.ijiis.20150401.14
AB  - In this paper we investigate the use of the feed-forward back propagation neural networks (FFBPNN) for automatic speech recognition of Arabic letters with their four vowels (Fatha, dhamma, Kasra, Soukoun). This investigation will constitute a basically step for the recognition of continuous Speech. Features were extracted from recorded corpus by using a variety of conventional methods such as Linear Predictive Codes (LPC), Perceptual Linear Prediction (PLP), Relative Spectral Perceptual Linear Prediction (RASTA-PLP), Mel Frequency Cepstral Coefficients (MFCC), Continuous Wavelet Transform (CWT), etc. Here, several hybrid methods have been used too. Since the extracted features have large dimensionalities they were reduced by conserving the most discriminatory information with the Principal Component Analysis (PCA) technique. The recognition performance has been improved particularly when we use the PLP method followed by PCA technique.
VL  - 4
IS  - 1
ER  -

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Author Information

Mohamed Hassine

LARATSI Lab, ENIM, University of Monastir, Monastir, Tunisia
Lotfi Boussaid

EμE Lab, FSM, University of Monastir, Monastir, Tunisia
Hassani Massouad

LARATSI Lab, ENIM, University of Monastir, Monastir, Tunisia

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Plain Text BibTeX RIS

APA Style

Mohamed Hassine, Lotfi Boussaid, Hassani Massouad. (2015). Hybrid Techniques for Arabic Letter Recognition. International Journal of Intelligent Information Systems, 4(1), 27-34. https://doi.org/10.11648/j.ijiis.20150401.14

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ACS Style

Mohamed Hassine; Lotfi Boussaid; Hassani Massouad. Hybrid Techniques for Arabic Letter Recognition. Int. J. Intell. Inf. Syst. 2015, 4(1), 27-34. doi: 10.11648/j.ijiis.20150401.14

Copy | Download

AMA Style

Mohamed Hassine, Lotfi Boussaid, Hassani Massouad. Hybrid Techniques for Arabic Letter Recognition. Int J Intell Inf Syst. 2015;4(1):27-34. doi: 10.11648/j.ijiis.20150401.14

Copy | Download

@article{10.11648/j.ijiis.20150401.14,
  author = {Mohamed Hassine and Lotfi Boussaid and Hassani Massouad},
  title = {Hybrid Techniques for Arabic Letter Recognition},
  journal = {International Journal of Intelligent Information Systems},
  volume = {4},
  number = {1},
  pages = {27-34},
  doi = {10.11648/j.ijiis.20150401.14},
  url = {https://doi.org/10.11648/j.ijiis.20150401.14},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ijiis.20150401.14},
  abstract = {In this paper we investigate the use of the feed-forward back propagation neural networks (FFBPNN) for automatic speech recognition of Arabic letters with their four vowels (Fatha, dhamma, Kasra, Soukoun). This investigation will constitute a basically step for the recognition of continuous Speech. Features were extracted from recorded corpus by using a variety of conventional methods such as Linear Predictive Codes (LPC), Perceptual Linear Prediction (PLP), Relative Spectral Perceptual Linear Prediction (RASTA-PLP), Mel Frequency Cepstral Coefficients (MFCC), Continuous Wavelet Transform (CWT), etc. Here, several hybrid methods have been used too. Since the extracted features have large dimensionalities they were reduced by conserving the most discriminatory information with the Principal Component Analysis (PCA) technique. The recognition performance has been improved particularly when we use the PLP method followed by PCA technique.},
 year = {2015}
}

Copy | Download

TY  - JOUR
T1  - Hybrid Techniques for Arabic Letter Recognition
AU  - Mohamed Hassine
AU  - Lotfi Boussaid
AU  - Hassani Massouad
Y1  - 2015/02/02
PY  - 2015
N1  - https://doi.org/10.11648/j.ijiis.20150401.14
DO  - 10.11648/j.ijiis.20150401.14
T2  - International Journal of Intelligent Information Systems
JF  - International Journal of Intelligent Information Systems
JO  - International Journal of Intelligent Information Systems
SP  - 27
EP  - 34
PB  - Science Publishing Group
SN  - 2328-7683
UR  - https://doi.org/10.11648/j.ijiis.20150401.14
AB  - In this paper we investigate the use of the feed-forward back propagation neural networks (FFBPNN) for automatic speech recognition of Arabic letters with their four vowels (Fatha, dhamma, Kasra, Soukoun). This investigation will constitute a basically step for the recognition of continuous Speech. Features were extracted from recorded corpus by using a variety of conventional methods such as Linear Predictive Codes (LPC), Perceptual Linear Prediction (PLP), Relative Spectral Perceptual Linear Prediction (RASTA-PLP), Mel Frequency Cepstral Coefficients (MFCC), Continuous Wavelet Transform (CWT), etc. Here, several hybrid methods have been used too. Since the extracted features have large dimensionalities they were reduced by conserving the most discriminatory information with the Principal Component Analysis (PCA) technique. The recognition performance has been improved particularly when we use the PLP method followed by PCA technique.
VL  - 4
IS  - 1
ER  -

Copy | Download