A Comprehensive Review on Features Extraction and Features Matching Techniques for Deception Detection
Over a few decades, a remarkable amount of research has been conducted in the field of speech signal processing particularly on deception detection for security applications. In this study, a comprehensive review on recent machine learning approaches using verbal and non-verbal features is presented...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
IEEE
2022-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/9730911/ |
| _version_ | 1828774012037103616 |
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| author | Sinead V. Fernandes Muhammad Sana Ullah |
| author_facet | Sinead V. Fernandes Muhammad Sana Ullah |
| author_sort | Sinead V. Fernandes |
| collection | DOAJ |
| description | Over a few decades, a remarkable amount of research has been conducted in the field of speech signal processing particularly on deception detection for security applications. In this study, a comprehensive review on recent machine learning approaches using verbal and non-verbal features is presented for deception detection. A brief overview on different feature extraction techniques, the results of recognition rate, and computational time based on machine learning methods are summarized in a tabular format. In addition, numerous datasets used as primary sources of deception detection in the review articles are also presented in this work. Key findings from the reviewed articles are summarized and a few major issues related to deception detection approaches are examined. A statistical analysis which conducted by extracting the significant information from the eighty-eight (88) scientific papers over the last thirty (30) years are provided in this review paper. The results emphasize on the trends of research in deception detection as well as further research opportunities for researchers as a part of continuous progress. |
| first_indexed | 2024-12-11T15:13:35Z |
| format | Article |
| id | doaj.art-e2fc6908a4e9414cb75a43dc0934ea66 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-11T15:13:35Z |
| publishDate | 2022-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-e2fc6908a4e9414cb75a43dc0934ea662022-12-22T01:00:41ZengIEEEIEEE Access2169-35362022-01-0110282332824610.1109/ACCESS.2022.31578219730911A Comprehensive Review on Features Extraction and Features Matching Techniques for Deception DetectionSinead V. Fernandes0https://orcid.org/0000-0002-8696-5377Muhammad Sana Ullah1https://orcid.org/0000-0003-4522-2814Department of Electrical and Computer Engineering, Florida Polytechnic University, Lakeland, FL, USADepartment of Electrical and Computer Engineering, Florida Polytechnic University, Lakeland, FL, USAOver a few decades, a remarkable amount of research has been conducted in the field of speech signal processing particularly on deception detection for security applications. In this study, a comprehensive review on recent machine learning approaches using verbal and non-verbal features is presented for deception detection. A brief overview on different feature extraction techniques, the results of recognition rate, and computational time based on machine learning methods are summarized in a tabular format. In addition, numerous datasets used as primary sources of deception detection in the review articles are also presented in this work. Key findings from the reviewed articles are summarized and a few major issues related to deception detection approaches are examined. A statistical analysis which conducted by extracting the significant information from the eighty-eight (88) scientific papers over the last thirty (30) years are provided in this review paper. The results emphasize on the trends of research in deception detection as well as further research opportunities for researchers as a part of continuous progress.https://ieeexplore.ieee.org/document/9730911/Deception detectionmachine learningnon-verbal featuresprincipal component analysisverbal features |
| spellingShingle | Sinead V. Fernandes Muhammad Sana Ullah A Comprehensive Review on Features Extraction and Features Matching Techniques for Deception Detection IEEE Access Deception detection machine learning non-verbal features principal component analysis verbal features |
| title | A Comprehensive Review on Features Extraction and Features Matching Techniques for Deception Detection |
| title_full | A Comprehensive Review on Features Extraction and Features Matching Techniques for Deception Detection |
| title_fullStr | A Comprehensive Review on Features Extraction and Features Matching Techniques for Deception Detection |
| title_full_unstemmed | A Comprehensive Review on Features Extraction and Features Matching Techniques for Deception Detection |
| title_short | A Comprehensive Review on Features Extraction and Features Matching Techniques for Deception Detection |
| title_sort | comprehensive review on features extraction and features matching techniques for deception detection |
| topic | Deception detection machine learning non-verbal features principal component analysis verbal features |
| url | https://ieeexplore.ieee.org/document/9730911/ |
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