Deep Neural Learning Adaptive Sequential Monte Carlo for Automatic Image and Speech Recognition
To enhance the performance of image classification and speech recognition, the optimizer is considered an important factor for achieving high accuracy. The state-of-the-art optimizer can perform to serve in applications that may not require very high accuracy, yet the demand for high-precision image...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2020-01-01
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| Series: | Applied Computational Intelligence and Soft Computing |
| Online Access: | http://dx.doi.org/10.1155/2020/8866259 |
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| _version_ | 1832547706443137024 |
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| author | Patcharin Kamsing Peerapong Torteeka Wuttichai Boonpook Chunxiang Cao |
| author_facet | Patcharin Kamsing Peerapong Torteeka Wuttichai Boonpook Chunxiang Cao |
| author_sort | Patcharin Kamsing |
| collection | DOAJ |
| description | To enhance the performance of image classification and speech recognition, the optimizer is considered an important factor for achieving high accuracy. The state-of-the-art optimizer can perform to serve in applications that may not require very high accuracy, yet the demand for high-precision image classification and speech recognition is increasing. This study implements an adaptive method for applying the particle filter technique with a gradient descent optimizer to improve model learning performance. Using a pretrained model helps reduce the computational time to deploy an image classification model and uses a simple deep convolutional neural network for speech recognition. The applied method results in a higher speech recognition accuracy score—89.693% for the test dataset—than the conventional method, which reaches 89.325%. The applied method also performs well on the image classification task, reaching an accuracy of 89.860% on the test dataset, better than the conventional method, which has an accuracy of 89.644%. Despite a slight difference in accuracy, the applied optimizer performs well in this dataset overall. |
| format | Article |
| id | doaj-art-64613111f58e4788bc5d49bb18e6a875 |
| institution | Kabale University |
| issn | 1687-9724 1687-9732 |
| language | English |
| publishDate | 2020-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Applied Computational Intelligence and Soft Computing |
| spelling | doaj-art-64613111f58e4788bc5d49bb18e6a8752025-02-03T06:43:37ZengWileyApplied Computational Intelligence and Soft Computing1687-97241687-97322020-01-01202010.1155/2020/88662598866259Deep Neural Learning Adaptive Sequential Monte Carlo for Automatic Image and Speech RecognitionPatcharin Kamsing0Peerapong Torteeka1Wuttichai Boonpook2Chunxiang Cao3Air-Space Control, Optimization and Management Laboratory, Department of Aeronautical Engineering, International Academy of Aviation Industry, King Mongkut’s Institute of Technology, Ladkrabang, Bangkok 10520, ThailandNational Astronomical Research Institute of Thailand, ChiangMai 50180, ThailandDepartment of Geography, Faculty of Social Sciences, Srinakharinwirot University, Bangkok 10110, ThailandState Key Laboratory of Remote Sensing Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100101, ChinaTo enhance the performance of image classification and speech recognition, the optimizer is considered an important factor for achieving high accuracy. The state-of-the-art optimizer can perform to serve in applications that may not require very high accuracy, yet the demand for high-precision image classification and speech recognition is increasing. This study implements an adaptive method for applying the particle filter technique with a gradient descent optimizer to improve model learning performance. Using a pretrained model helps reduce the computational time to deploy an image classification model and uses a simple deep convolutional neural network for speech recognition. The applied method results in a higher speech recognition accuracy score—89.693% for the test dataset—than the conventional method, which reaches 89.325%. The applied method also performs well on the image classification task, reaching an accuracy of 89.860% on the test dataset, better than the conventional method, which has an accuracy of 89.644%. Despite a slight difference in accuracy, the applied optimizer performs well in this dataset overall.http://dx.doi.org/10.1155/2020/8866259 |
| spellingShingle | Patcharin Kamsing Peerapong Torteeka Wuttichai Boonpook Chunxiang Cao Deep Neural Learning Adaptive Sequential Monte Carlo for Automatic Image and Speech Recognition Applied Computational Intelligence and Soft Computing |
| title | Deep Neural Learning Adaptive Sequential Monte Carlo for Automatic Image and Speech Recognition |
| title_full | Deep Neural Learning Adaptive Sequential Monte Carlo for Automatic Image and Speech Recognition |
| title_fullStr | Deep Neural Learning Adaptive Sequential Monte Carlo for Automatic Image and Speech Recognition |
| title_full_unstemmed | Deep Neural Learning Adaptive Sequential Monte Carlo for Automatic Image and Speech Recognition |
| title_short | Deep Neural Learning Adaptive Sequential Monte Carlo for Automatic Image and Speech Recognition |
| title_sort | deep neural learning adaptive sequential monte carlo for automatic image and speech recognition |
| url | http://dx.doi.org/10.1155/2020/8866259 |
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