Applications of cascade-forward neural networks for nasal, lateral and trill arabic phonemes
In the field of speech recognition using Artificial Neural Network (ANN) system, a lot of research has been done and ongoing research is looking for better algorithm to improve the existing recognition methods. In this paper, we monitored and analyzed the performance of multi-layer feed-forward with...
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my.utm.345362017-02-02T01:15:14Z http://eprints.utm.my/id/eprint/34536/ Applications of cascade-forward neural networks for nasal, lateral and trill arabic phonemes Abdul-Kadir, Nurul Ashikin Sudirman, Rubita Mahmood, Nasrul Humaimi Ahmad, Abdul Hamid TK Electrical engineering. Electronics Nuclear engineering In the field of speech recognition using Artificial Neural Network (ANN) system, a lot of research has been done and ongoing research is looking for better algorithm to improve the existing recognition methods. In this paper, we monitored and analyzed the performance of multi-layer feed-forward with back-propagation (MLFFBP) and cascade-forward (CF) networks on our phoneme recognition system of Standard Arabic (SA). This study focused on Malaysian children as test subjects. It is focused on four chosen phonemes from SA, which composed of nasal, lateral and trill behaviors, i.e. tabulated at four different articulation places. The highest training recognition rate for multi-layer and cascade-layer network are 98.8 % and 95.2 % respectively, while the highest testing recognition rate achieved for both networks is 92.9 % for all four phonemes under study. AICIT 2012 Book Section PeerReviewed Abdul-Kadir, Nurul Ashikin and Sudirman, Rubita and Mahmood, Nasrul Humaimi and Ahmad, Abdul Hamid (2012) Applications of cascade-forward neural networks for nasal, lateral and trill arabic phonemes. In: Proceedings - ICIDT 2012, 8th International Conference on Information Science and Digital Content Technology. AICIT, pp. 495-499. ISBN 978-898867869-5 http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6269323 |
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TK Electrical engineering. Electronics Nuclear engineering Abdul-Kadir, Nurul Ashikin Sudirman, Rubita Mahmood, Nasrul Humaimi Ahmad, Abdul Hamid Applications of cascade-forward neural networks for nasal, lateral and trill arabic phonemes |
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In the field of speech recognition using Artificial Neural Network (ANN) system, a lot of research has been done and ongoing research is looking for better algorithm to improve the existing recognition methods. In this paper, we monitored and analyzed the performance of multi-layer feed-forward with back-propagation (MLFFBP) and cascade-forward (CF) networks on our phoneme recognition system of Standard Arabic (SA). This study focused on Malaysian children as test subjects. It is focused on four chosen phonemes from SA, which composed of nasal, lateral and trill behaviors, i.e. tabulated at four different articulation places. The highest training recognition rate for multi-layer and cascade-layer network are 98.8 % and 95.2 % respectively, while the highest testing recognition rate achieved for both networks is 92.9 % for all four phonemes under study. |
format |
Book Section |
author |
Abdul-Kadir, Nurul Ashikin Sudirman, Rubita Mahmood, Nasrul Humaimi Ahmad, Abdul Hamid |
author_facet |
Abdul-Kadir, Nurul Ashikin Sudirman, Rubita Mahmood, Nasrul Humaimi Ahmad, Abdul Hamid |
author_sort |
Abdul-Kadir, Nurul Ashikin |
title |
Applications of cascade-forward neural networks for nasal, lateral and trill arabic phonemes |
title_short |
Applications of cascade-forward neural networks for nasal, lateral and trill arabic phonemes |
title_full |
Applications of cascade-forward neural networks for nasal, lateral and trill arabic phonemes |
title_fullStr |
Applications of cascade-forward neural networks for nasal, lateral and trill arabic phonemes |
title_full_unstemmed |
Applications of cascade-forward neural networks for nasal, lateral and trill arabic phonemes |
title_sort |
applications of cascade-forward neural networks for nasal, lateral and trill arabic phonemes |
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AICIT |
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2012 |
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http://eprints.utm.my/id/eprint/34536/ http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6269323 |
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13.18916 |