Classification of emotion in spoken Finnish using vowel-length segments: Increasing reliability with a fusion technique

Authors:
Eero Väyrynen;Juhani Toivanen;Tapio Seppänen
Affiliations:
University of Oulu, Department of Electrical and Information Engineering, Computer Engineering Laboratory, P.O. Box 4500, FI-90014 Oulu, Finland;Academy of Finland and University of Oulu, Department of Electrical and Information Engineering, Information Processing Laboratory, P.O. Box 4500, FI-90014 Oulu, Finland;University of Oulu, Department of Electrical and Information Engineering, Computer Engineering Laboratory, P.O. Box 4500, FI-90014 Oulu, Finland
Venue:
Speech Communication
Year:
2011

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Abstract

Classification of emotional content of short Finnish emotional [a:] vowel speech samples is performed using vocal source parameter and traditional intonation contour parameter derived prosodic features. A multiple kNN classifier based decision level fusion classification architecture is proposed for multimodal speech prosody and vocal source expert fusion. The sum fusion rule and the sequential forward floating search (SFFS) algorithm are used to produce leveraged expert classifiers. Automatic classification tests in five emotional classes demonstrate that significantly higher than random level emotional content classification performance is achievable using both prosodic and vocal source features. The fusion classification approach is further shown to be capable of emotional content classification in the vowel domain approaching the performance level of the human reference.