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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Shaukat, A. Ke Chen |
| Copyright Year | 2011 |
| Description | Author affiliation: School of Computer Science, The University of Manchester, M13 9PL, U.K. (Ke Chen) || Department of Computer Engineering, College of EME, National University of Sciences and Technology, Rawalpindi, 46000, Pakistan (Shaukat, A.) |
| Abstract | This paper presents our investigations on automatic emotional state recognition from speech signals using ensemble based methods based on different acoustic representations/feature measures. In our work, we employ various types of acoustic feature measures where none of the feature measures is optimal for emotional state classification. It is observed that different feature measures may be complementary and used simultaneously to yield a robust classification performance. Therefore, we employ a probabilistic method of combining classifiers based on different feature measures. The combination method that uses different feature measures simultaneously yields high recognition rates on various emotional speech corpora for both full feature set and language-independent feature subset. The ensemble method also outperforms a composite-feature representation and two other methods reported in literature. In addition, the classification accuracies achieved by our combination method are competitive with those mentioned in literature for different emotional speech corpora. |
| Starting Page | 1910 |
| Ending Page | 1917 |
| File Size | 853552 |
| Page Count | 8 |
| File Format | |
| ISBN | 9781424496358 |
| ISSN | 21614407 |
| e-ISBN | 9781424496372 |
| DOI | 10.1109/IJCNN.2011.6033457 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2011-07-31 |
| Publisher Place | USA |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Speech Speech recognition Support vector machines Emotion recognition Acoustics Feature extraction Frequency measurement |
| Content Type | Text |
| Resource Type | Article |
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