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Speaker accent recognition through statistical descriptors of Mel-bands spectral energy and neural network model

Proceeding of the 3rd IEEE Conference on Sustainable Utilization and Development in Engineering and Technology (STUDENT) 2012 at Kuala Lumpur, Malaysia on 6 October 2012 through 9 October 2012. Link to publisher's homepage at http://ezproxy.unimap.edu.my:2080/Xplore/dynhome.jsp

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Bibliographic Details
Main Authors: Yusnita, Mohd Ali, Pandiyan, Paulraj Murugesa, Prof. Dr., Sazali, Yaacob, Prof. Dr., Shahriman, Abu Bakar, Dr., Nataraj, Sathees Kumar
Other Authors: yusnita082@ppinang.uitm.edu.my
Format: Working Paper
Language:English
Published: IEEE Conference Publications 2014
Subjects:
Accent recognition
Mel-bands
Neural network
Spectral energy
Statistical analysis
Online Access:http://dspace.unimap.edu.my:80/dspace/handle/123456789/35449
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spelling my.unimap-354492014-06-12T16:40:25Z Speaker accent recognition through statistical descriptors of Mel-bands spectral energy and neural network model Yusnita, Mohd Ali Pandiyan, Paulraj Murugesa, Prof. Dr. Sazali, Yaacob, Prof. Dr. Shahriman, Abu Bakar, Dr. Nataraj, Sathees Kumar yusnita082@ppinang.uitm.edu.my paul@unimap.edu.my s.yaacob@unimap.edu.my shahriman@unimap.edu.my Accent recognition Mel-bands Neural network Spectral energy Statistical analysis Proceeding of the 3rd IEEE Conference on Sustainable Utilization and Development in Engineering and Technology (STUDENT) 2012 at Kuala Lumpur, Malaysia on 6 October 2012 through 9 October 2012. Link to publisher's homepage at http://ezproxy.unimap.edu.my:2080/Xplore/dynhome.jsp Accent recognition is one of the most important topics in automatic speaker and speaker-independent speech recognition (SI-ASR) systems in recent years. The growth of voice-controlled technologies has becoming part of our daily life, nevertheless variability in speech makes these spoken language technologies relatively difficult. One of the profound variability is accent. By classifying accent types, different models could be developed to handle SI-ASR. In this paper, we classified three accents in English language recorded from three main ethnicities in Malaysia namely Malay, Chinese and Indian using artificial neural network model. All experiments were performed in speaker-independent and three most accent-sensitive words-independent modes. Mel-bands spectral energy was extracted from eighteen bands taking the statistical values of each speech sample i.e. mean, standard deviation, kurtosis and the ratio of standard deviation to kurtosis to characterize the spectral energy distribution. The system was evaluated using independent test dataset, partial-independent test dataset and training dataset. The best three-class accuracy rate of 99.01% with independent test dataset was obtained. The overall accuracy rate for several trials was averaged to 96.79% with the average learning time at 49 epochs. 2014-06-12T16:40:25Z 2014-06-12T16:40:25Z 2012-10 Working Paper p. 262-267 978-1-4673-1649-1 (Print) 978-1-4673-1704-7 (Online) 1985-5753 http://ieeexplore.ieee.org/xpl/articleDetails.jsp?tp=&arnumber=6408416 http://dspace.unimap.edu.my:80/dspace/handle/123456789/35449 http://dx.doi.org/10.1109/STUDENT.2012.6408416 en Proceeding of The 3rd IEEE Conference on Sustainable Utilization and Development in Engineering and Technology (STUDENT 2012); IEEE Conference Publications
institution Universiti Malaysia Perlis
building UniMAP Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Malaysia Perlis
content_source UniMAP Library Digital Repository
url_provider http://dspace.unimap.edu.my/
language English
topic Accent recognition
Mel-bands
Neural network
Spectral energy
Statistical analysis
spellingShingle Accent recognition
Mel-bands
Neural network
Spectral energy
Statistical analysis
Yusnita, Mohd Ali
Pandiyan, Paulraj Murugesa, Prof. Dr.
Sazali, Yaacob, Prof. Dr.
Shahriman, Abu Bakar, Dr.
Nataraj, Sathees Kumar
Speaker accent recognition through statistical descriptors of Mel-bands spectral energy and neural network model
description Proceeding of the 3rd IEEE Conference on Sustainable Utilization and Development in Engineering and Technology (STUDENT) 2012 at Kuala Lumpur, Malaysia on 6 October 2012 through 9 October 2012. Link to publisher's homepage at http://ezproxy.unimap.edu.my:2080/Xplore/dynhome.jsp
author2 yusnita082@ppinang.uitm.edu.my
author_facet yusnita082@ppinang.uitm.edu.my
Yusnita, Mohd Ali
Pandiyan, Paulraj Murugesa, Prof. Dr.
Sazali, Yaacob, Prof. Dr.
Shahriman, Abu Bakar, Dr.
Nataraj, Sathees Kumar
format Working Paper
author Yusnita, Mohd Ali
Pandiyan, Paulraj Murugesa, Prof. Dr.
Sazali, Yaacob, Prof. Dr.
Shahriman, Abu Bakar, Dr.
Nataraj, Sathees Kumar
author_sort Yusnita, Mohd Ali
title Speaker accent recognition through statistical descriptors of Mel-bands spectral energy and neural network model
title_short Speaker accent recognition through statistical descriptors of Mel-bands spectral energy and neural network model
title_full Speaker accent recognition through statistical descriptors of Mel-bands spectral energy and neural network model
title_fullStr Speaker accent recognition through statistical descriptors of Mel-bands spectral energy and neural network model
title_full_unstemmed Speaker accent recognition through statistical descriptors of Mel-bands spectral energy and neural network model
title_sort speaker accent recognition through statistical descriptors of mel-bands spectral energy and neural network model
publisher IEEE Conference Publications
publishDate 2014
url http://dspace.unimap.edu.my:80/dspace/handle/123456789/35449
_version_ 1643797802439933952
score 13.226497

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