%0 Conference Paper %1 conf/etra/BeeldersB10 %A Javidi, Mohammad Masoud %A Roshan, Ebrahim Fazlizadeh %B Applied Mathematics and Sciences: An International Journal (MathSJ) %D 2014 %E Morimoto, Carlos Hitoshi %E Istance, Howell O. %E Hyrskykari, Aulikki %E Ji, Qiang %I ACM %K C5.0 Cepstral Coefficients Emotion Feature Frequency Machines Mel-scale Support Vector extraction recognition %N 2 %P 21-33 %T Speech Emotion Recognition by Using Combinations of Support Vector Machine (SVM) and C5.0 %U https://airccse.com/mathsj/papers/1214mathsj02.pdf %V 1 %X Speech emotion recognition enables a computer system to records sounds and realizes the emotion of the speaker. we are still far from having a natural interaction between the human and machine because machines cannot distinguishes the emotion of the speaker. For this reason it has been established a new investigation field, namely “the speech emotion recognition systems”. The accuracy of these systems depend on the various factors such as the type and the number of the emotion states and also the classifier type. In this paper, the classification methods of C5.0, Support Vector Machine (SVM), and the combination of C5.0 and SVM (SVM-C5.0) are verified, and their efficiencies in speech emotion recognition are compared. The utilized features in this research include energy, Zero Crossing Rate (ZCR), pitch, and Mel-scale Frequency Cepstral Coefficients (MFCC). The results of paper demonstrate that the effectiveness proposed SVM-C5.0 classification method is more efficient in recognizing the emotion of the between -5.5 % and 8.9 % depending on the number of emotion states than SVM, C5.0. %@ 978-1-60558-994-7

@inproceedings{conf/etra/BeeldersB10, abstract = {Speech emotion recognition enables a computer system to records sounds and realizes the emotion of the speaker. we are still far from having a natural interaction between the human and machine because machines cannot distinguishes the emotion of the speaker. For this reason it has been established a new investigation field, namely “the speech emotion recognition systems”. The accuracy of these systems depend on the various factors such as the type and the number of the emotion states and also the classifier type. In this paper, the classification methods of C5.0, Support Vector Machine (SVM), and the combination of C5.0 and SVM (SVM-C5.0) are verified, and their efficiencies in speech emotion recognition are compared. The utilized features in this research include energy, Zero Crossing Rate (ZCR), pitch, and Mel-scale Frequency Cepstral Coefficients (MFCC). The results of paper demonstrate that the effectiveness proposed SVM-C5.0 classification method is more efficient in recognizing the emotion of the between -5.5 % and 8.9 % depending on the number of emotion states than SVM, C5.0. }, added-at = {2022-03-17T10:39:50.000+0100}, author = {Javidi, Mohammad Masoud and Roshan, Ebrahim Fazlizadeh}, biburl = {https://www.bibsonomy.org/bibtex/2730189830b45c7ea98b59ebd89717673/journalmathsj}, booktitle = {Applied Mathematics and Sciences: An International Journal (MathSJ)}, crossref = {conf/etra/2010}, editor = {Morimoto, Carlos Hitoshi and Istance, Howell O. and Hyrskykari, Aulikki and Ji, Qiang}, ee = {https://doi.org/10.1145/1743666.1743709}, interhash = {c865b3fe3b1308c97672980d9e4f1d93}, intrahash = {730189830b45c7ea98b59ebd89717673}, isbn = {978-1-60558-994-7}, issn = {2349 - 6223}, keywords = {C5.0 Cepstral Coefficients Emotion Feature Frequency Machines Mel-scale Support Vector extraction recognition}, language = {English}, month = aug, number = 2, pages = {21-33}, publisher = {ACM}, timestamp = {2022-03-17T10:39:50.000+0100}, title = {Speech Emotion Recognition by Using Combinations of Support Vector Machine (SVM) and C5.0}, url = {https://airccse.com/mathsj/papers/1214mathsj02.pdf}, volume = 1, year = 2014 }