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Öğe Boosting selection of speech related features to improve performance of multi-class SVMs in emotion detection(PERGAMON-ELSEVIER SCIENCE LTD, 2009) Altun, Halis; Polat, GoekhanThis paper deals with the strategies for feature selection and multi-class classification in the emotion detection problem. The aim is two-fold: to increase the effectiveness of four feature selection algorithms and to improve accuracy of multi-class Classifiers for emotion detection problem under different frameworks and strategies. Although, a large amount of research has been conducted to determine the most informative features in emotion detection, it is still an open problem to identify reliably discriminating features. As it is believed that highly informative features are more critical factor than classifier itself, recent Studies have been focused oil identifying the features that contribute more to the classification problem. In this paper, in order to improve the performance of multi-class SVMs in emotion detection, 58 features extracted from recorded speech samples are processed in two new frameworks to boost the feature selection algorithms. Evaluation of the final feature sets validates that the frameworks are able to select more informative Subset of the features in terms of class-separability. Also it is found that among four feature selection algorithms, a recently proposed one, LSBOUND, significantly outperforms the others. The accuracy rate obtained in the proposed framework is the highest achievement reported so far in the literature for the same dataset. (C) 2008 Elsevier Ltd. All rights reserved.Öğe Determining efficiency of speech feature groups in emotion detection(IEEE, 2007) Polat, Goekhan; Altun, HalisFeatures, extract from speech parameter are frequently used in emotion detection problem. Prosodic, MFCC, LPC and band energy feature groups are commonly used in literature to detect emotion in speech. The aim of the study is to examine the efficiency of these features groups in emotion detection problem using a SVM classifier.Öğe Evalutation of performance of KNN, MLP and RBF classifiers in emotion detection problem(IEEE, 2007) Polat, Goekhan; Altun, HalisEmotion Detection has gained increasing attention and become an active research area. The problem is solved with improved feature set with different number of feature groups, by employing different classifiers in order to achieve satisfactory recognition rate. In this study, speech related features are employed to evaluate the performance of different classifiers in emotion detection problem.Öğe New frameworks to boost feature selection algorithms in emotion detection for improved human-computer interaction(SPRINGER-VERLAG BERLIN, 2007) Altun, Halis; Polat, Goekhan; Mele, F; Santillo, S; Ramella, G; Ventriglia, FOne of the primary aims in human-computer interaction research is to develop an ability to recognize affective state of the user. Such ability is indispensable to have a more human-like nature in human-computer interaction. However, the researches in this direction are not mature and intensive efforts have only been witnessed recently. This work envisages the possibility of enhancing feature selection phase of emotion detection task to obtain robust parameters which will be determined from verbal information to achieve an improved affective human-computer interaction. As highly informative feature selection is believed to be a more critical factor than classifier itself, recent studies have increasingly focussed on determining features that contribute more to the classification problem. Two new frameworks for multi-class emotion detection problem are proposed in this paper, so as to boost the feature selection algorithms in a way that the selected features will be more informative in terms of class-separability. Evaluation of the selected final features is accomplished by multi-class classifiers. Results show that the proposed frameworks are successful in terms of attaining lower average cross-validation error.Öğe On the comparison of classifiers' performance in emotion classification: Critiques and Suggestions(IEEE, 2008) Altun, Halis; Polat, GoekhanIn literature there is a huge body of references available which compare various classifiers in a particular application. However, the reliability of such a comparison is only valid if the model parameters, performance criteria and training environment are chosen in a fair framework, as successful application of a classifier is dependent on the those parameters. In this study we attempt to answer the questions below in a emotion detection framework, using classifiers such as KNN, SVM, RBF and MLP: Is the success of a classifier enough to make the claim that a classifier is "the best one" in a particular classification task? How is it possible to carry out a fair comparison between classifiers?
