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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IEEE Conference Publications
2014
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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 |
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Universiti Malaysia Perlis |
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Asia |
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Malaysia |
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Universiti Malaysia Perlis |
| content_source |
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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 |
13.19449 |