Towards a Framework for Acquisition and Analysis of Speeches to Identify Suspicious Contents through Machine Learning
The most prominent form of human communication and interaction is speech. It plays an indispensable role for expressing emotions, motivating, guiding, and cheering. An ill-intentioned speech can mislead people, societies, and even a nation. A misguided speech can trigger social controversy and can r...
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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: | Complexity |
| Online Access: | http://dx.doi.org/10.1155/2020/5639787 |
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| author | Md. Rashadur Rahman Mohammad Shamsul Arefin Md. Billal Hossain Mohammad Ashfak Habib A. S. M. Kayes |
| author_facet | Md. Rashadur Rahman Mohammad Shamsul Arefin Md. Billal Hossain Mohammad Ashfak Habib A. S. M. Kayes |
| author_sort | Md. Rashadur Rahman |
| collection | DOAJ |
| description | The most prominent form of human communication and interaction is speech. It plays an indispensable role for expressing emotions, motivating, guiding, and cheering. An ill-intentioned speech can mislead people, societies, and even a nation. A misguided speech can trigger social controversy and can result in violent activities. Every day, there are a lot of speeches being delivered around the world, which are quite impractical to inspect manually. In order to prevent any vicious action resulting from any misguided speech, the development of an automatic system that can efficiently detect suspicious speech has become imperative. In this study, we have presented a framework for acquisition of speech along with the location of the speaker, converting the speeches into texts and, finally, we have proposed a system based on long short-term memory (LSTM) which is a variant of recurrent neural network (RNN) to classify speeches into suspicious and nonsuspicious. We have considered speeches of Bangla language and developed our own dataset that contains about 5000 suspicious and nonsuspicious samples for training and validating our model. A comparative analysis of accuracy among other machine learning algorithms such as logistic regression, SVM, KNN, Naive Bayes, and decision tree is performed in order to evaluate the effectiveness of the system. The experimental results show that our proposed deep learning-based model provides the highest accuracy compared to other algorithms. |
| format | Article |
| id | doaj-art-427dc74b10f845febbf20953f9db206f |
| institution | OA Journals |
| issn | 1076-2787 1099-0526 |
| language | English |
| publishDate | 2020-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Complexity |
| spelling | doaj-art-427dc74b10f845febbf20953f9db206f2025-08-20T02:37:51ZengWileyComplexity1076-27871099-05262020-01-01202010.1155/2020/56397875639787Towards a Framework for Acquisition and Analysis of Speeches to Identify Suspicious Contents through Machine LearningMd. Rashadur Rahman0Mohammad Shamsul Arefin1Md. Billal Hossain2Mohammad Ashfak Habib3A. S. M. Kayes4Department of Computer Science & Engineering, Chittagong University of Engineering & Technology, Chattogram, BangladeshDepartment of Computer Science & Engineering, Chittagong University of Engineering & Technology, Chattogram, BangladeshDepartment of Computer Science & Engineering, Chittagong University of Engineering & Technology, Chattogram, BangladeshDepartment of Computer Science & Engineering, Chittagong University of Engineering & Technology, Chattogram, BangladeshDepartment of Computer Science & Information Technology, La Trobe University, Melbourne, AustraliaThe most prominent form of human communication and interaction is speech. It plays an indispensable role for expressing emotions, motivating, guiding, and cheering. An ill-intentioned speech can mislead people, societies, and even a nation. A misguided speech can trigger social controversy and can result in violent activities. Every day, there are a lot of speeches being delivered around the world, which are quite impractical to inspect manually. In order to prevent any vicious action resulting from any misguided speech, the development of an automatic system that can efficiently detect suspicious speech has become imperative. In this study, we have presented a framework for acquisition of speech along with the location of the speaker, converting the speeches into texts and, finally, we have proposed a system based on long short-term memory (LSTM) which is a variant of recurrent neural network (RNN) to classify speeches into suspicious and nonsuspicious. We have considered speeches of Bangla language and developed our own dataset that contains about 5000 suspicious and nonsuspicious samples for training and validating our model. A comparative analysis of accuracy among other machine learning algorithms such as logistic regression, SVM, KNN, Naive Bayes, and decision tree is performed in order to evaluate the effectiveness of the system. The experimental results show that our proposed deep learning-based model provides the highest accuracy compared to other algorithms.http://dx.doi.org/10.1155/2020/5639787 |
| spellingShingle | Md. Rashadur Rahman Mohammad Shamsul Arefin Md. Billal Hossain Mohammad Ashfak Habib A. S. M. Kayes Towards a Framework for Acquisition and Analysis of Speeches to Identify Suspicious Contents through Machine Learning Complexity |
| title | Towards a Framework for Acquisition and Analysis of Speeches to Identify Suspicious Contents through Machine Learning |
| title_full | Towards a Framework for Acquisition and Analysis of Speeches to Identify Suspicious Contents through Machine Learning |
| title_fullStr | Towards a Framework for Acquisition and Analysis of Speeches to Identify Suspicious Contents through Machine Learning |
| title_full_unstemmed | Towards a Framework for Acquisition and Analysis of Speeches to Identify Suspicious Contents through Machine Learning |
| title_short | Towards a Framework for Acquisition and Analysis of Speeches to Identify Suspicious Contents through Machine Learning |
| title_sort | towards a framework for acquisition and analysis of speeches to identify suspicious contents through machine learning |
| url | http://dx.doi.org/10.1155/2020/5639787 |
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