Emotion Identification Using Extremely Low Frequency Components of Speech Feature Contours
The investigations of emotional speech identification can be divided into two main parts, features and classifiers. In this paper, how to extract an effective speech feature set for the emotional speech identification is addressed. In our speech feature set, we use not only statistical analysis of f...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2014-01-01
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| Series: | The Scientific World Journal |
| Online Access: | http://dx.doi.org/10.1155/2014/757121 |
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| author | Chang-Hong Lin Wei-Kai Liao Wen-Chi Hsieh Wei-Jiun Liao Jia-Ching Wang |
| author_facet | Chang-Hong Lin Wei-Kai Liao Wen-Chi Hsieh Wei-Jiun Liao Jia-Ching Wang |
| author_sort | Chang-Hong Lin |
| collection | DOAJ |
| description | The investigations of emotional speech identification can be divided into two main parts, features and classifiers. In this paper, how to extract an effective speech feature set for the emotional speech identification is addressed. In our speech feature set, we use not only statistical analysis of frame-based acoustical features, but also the approximated speech feature contours, which are obtained by extracting extremely low frequency components to speech feature contours. Furthermore, principal component analysis (PCA) is applied to the approximated speech feature contours so that an efficient representation of approximated contours can be derived. The proposed speech feature set is fed into support vector machines (SVMs) to perform multiclass emotion identification. The experimental results demonstrate the performance of the proposed system with 82.26% identification rate. |
| format | Article |
| id | doaj-art-f8e7c1a49f1f40b88201767a69423f7e |
| institution | Kabale University |
| issn | 2356-6140 1537-744X |
| language | English |
| publishDate | 2014-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | The Scientific World Journal |
| spelling | doaj-art-f8e7c1a49f1f40b88201767a69423f7e2025-02-03T05:43:54ZengWileyThe Scientific World Journal2356-61401537-744X2014-01-01201410.1155/2014/757121757121Emotion Identification Using Extremely Low Frequency Components of Speech Feature ContoursChang-Hong Lin0Wei-Kai Liao1Wen-Chi Hsieh2Wei-Jiun Liao3Jia-Ching Wang4Department of Computer Science and Information Engineering, National Central University, TaiwanDepartment of Computer Science and Information Engineering, National Central University, TaiwanDepartment of Computer Science and Information Engineering, National Central University, TaiwanDepartment of Computer Science and Information Engineering, National Central University, TaiwanDepartment of Computer Science and Information Engineering, National Central University, TaiwanThe investigations of emotional speech identification can be divided into two main parts, features and classifiers. In this paper, how to extract an effective speech feature set for the emotional speech identification is addressed. In our speech feature set, we use not only statistical analysis of frame-based acoustical features, but also the approximated speech feature contours, which are obtained by extracting extremely low frequency components to speech feature contours. Furthermore, principal component analysis (PCA) is applied to the approximated speech feature contours so that an efficient representation of approximated contours can be derived. The proposed speech feature set is fed into support vector machines (SVMs) to perform multiclass emotion identification. The experimental results demonstrate the performance of the proposed system with 82.26% identification rate.http://dx.doi.org/10.1155/2014/757121 |
| spellingShingle | Chang-Hong Lin Wei-Kai Liao Wen-Chi Hsieh Wei-Jiun Liao Jia-Ching Wang Emotion Identification Using Extremely Low Frequency Components of Speech Feature Contours The Scientific World Journal |
| title | Emotion Identification Using Extremely Low Frequency Components of Speech Feature Contours |
| title_full | Emotion Identification Using Extremely Low Frequency Components of Speech Feature Contours |
| title_fullStr | Emotion Identification Using Extremely Low Frequency Components of Speech Feature Contours |
| title_full_unstemmed | Emotion Identification Using Extremely Low Frequency Components of Speech Feature Contours |
| title_short | Emotion Identification Using Extremely Low Frequency Components of Speech Feature Contours |
| title_sort | emotion identification using extremely low frequency components of speech feature contours |
| url | http://dx.doi.org/10.1155/2014/757121 |
| work_keys_str_mv | AT changhonglin emotionidentificationusingextremelylowfrequencycomponentsofspeechfeaturecontours AT weikailiao emotionidentificationusingextremelylowfrequencycomponentsofspeechfeaturecontours AT wenchihsieh emotionidentificationusingextremelylowfrequencycomponentsofspeechfeaturecontours AT weijiunliao emotionidentificationusingextremelylowfrequencycomponentsofspeechfeaturecontours AT jiachingwang emotionidentificationusingextremelylowfrequencycomponentsofspeechfeaturecontours |