Fast training of support vector machines using sequential minimal optimization
Advances in kernel methods
Toward Machine Emotional Intelligence: Analysis of Affective Physiological State
IEEE Transactions on Pattern Analysis and Machine Intelligence - Graph Algorithms and Computer Vision
Feature selection in data mining
Data mining
An introduction to variable and feature selection
The Journal of Machine Learning Research
Machine Learning
Early versus late fusion in semantic video analysis
Proceedings of the 13th annual ACM international conference on Multimedia
Early versus late fusion in semantic video analysis
Proceedings of the 13th annual ACM international conference on Multimedia
Data Mining: Practical Machine Learning Tools and Techniques, Second Edition (Morgan Kaufmann Series in Data Management Systems)
A Survey of Affect Recognition Methods: Audio, Visual, and Spontaneous Expressions
IEEE Transactions on Pattern Analysis and Machine Intelligence
Enhancing emotion recognition from speech through feature selection
TSD'10 Proceedings of the 13th international conference on Text, speech and dialogue
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This paper presents an approach to improve emotion recognition from spontaneous speech. We used a wrapper method to reduce an acoustic set of features and feature-level fusion to merge them with a set of linguistic ones. The proposed system was evaluated with the FAU Aibo Corpus. We considered the same emotion set that was proposed in the Interspeech 2009 Emotion Challenge. The main contribution of this work is the improvement, with the reduced set of features, of the results obtained in this Challenge and the combination of the best ones. We built this set with a selection of 28 acoustic and 5 linguistic features and concatenation of the feature vectors from an original set of 389 parameters.