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Despite numerous studies during the last decade speech emotion recognition is still the task of limited success. Great efforts were made for extending emotional speech feature sets and selecting the most effective ones, proposing multi-stage and multiple classifier based classification schemes, and developing multi-modal speech emotion recognition technique. Nevertheless, the reported emotion recognition...
This paper presents the experimental study of multi-stage classification based recognition of Lithuanian speech emotions. Three different criteria for feature selection were compared for this purpose: Maximal Efficiency, Minimal Cross-Correlation feature criterions, and the Sequential Feature Selection. A large database of spoken emotional Lithuanian language was used in this experiment - each of...
Feature selection is very relevant for speech emotion recognition task. Still, there is no consensus on optimal feature set and classification scheme for this task. Sequential forward selection (SFS) technique for multistage emotion classification scheme is proposed in this paper. Feature sets were formed from initial collection of 6552 speech emotion features. Experimental study was performed using...
The problem of speech emotion recognition commonly is dealt with by delivering a huge feature set containing up to a few thousands different features. This can raise the “curse of dimensionality” problem and downgrade speech emotion classification process. In this paper we present minimal cross-correlation based formation of multi-level features for speech emotion classification. The feature set is...
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