C
Constantine Kotropoulos
Researcher at Aristotle University of Thessaloniki
Publications - 251
Citations - 6212
Constantine Kotropoulos is an academic researcher from Aristotle University of Thessaloniki. The author has contributed to research in topics: Support vector machine & Feature vector. The author has an hindex of 41, co-authored 245 publications receiving 5869 citations.
Papers
More filters
Journal ArticleDOI
Emotional speech recognition: Resources, features, and methods
TL;DR: This paper overviews emotional speech recognition having in mind three goals to provide an up-to-date record of the available emotional speech data collections, and examines separately classification techniques that exploit timing information from which that ignore it.
Journal ArticleDOI
Using support vector machines to enhance the performance of elastic graph matching for frontal face authentication
TL;DR: A novel approach that reformulates Fisher's discriminant ratio to a quadratic optimization problem subject to a set of inequality constraints by combining statistical pattern recognition and support vector machines is proposed.
Proceedings ArticleDOI
Automatic emotional speech classification
TL;DR: The major contribution of the paper is to rate the discriminating capability of a set of features for emotional speech recognition, a useful tool which can be used in psychology to automatically classify utterances into five emotional states.
Proceedings ArticleDOI
Rule-based face detection in frontal views
TL;DR: A rule-based face detection algorithm in frontal views is developed that is applied to frontal views extracted from the European ACTS M2VTS database that contains the videosequences of 37 different persons and found that the algorithm provides a correct facial candidate in all cases.
Journal ArticleDOI
Fast and accurate sequential floating forward feature selection with the Bayes classifier applied to speech emotion recognition
TL;DR: A theoretical analysis that models the number of correctly classified utterances as a hypergeometric random variable enables the derivation of an accurate estimate of the variance of the correct classification rate during cross-validation by employing a fast SFFS variant.