Thanks to the recent progresses in judicial proceedings management, especially related to the introduction of audio/video recording systems, semantic retrieval is a key challenge. In this context emotion recognition engine, through the analysis of vocal signature of actors involved in judicial proceedings, could provide useful annotations for semantic retrieval of multimedia clips. With respect to the generation of semantic emotional tag in judicial domain, two main contributions are given: (1) the construction of an Italian emotional database for Italian proceedings annotation; (2) the investigation of a hierarchical classification system, based on risk minimization method, able to recognize emotional states from vocal signatures. In order to estimate the degree of affection we compared the proposed classification method with SVM, K-Nearest Neighbors and Naive Bayes, highlighting in terms of classification accuracy, the improvements given by a hierarchical learning approach. © 2009 Springer Berlin Heidelberg.

Fersini, E., Messina, V., Arosio, G., Archetti, F. (2009). Audio-based emotion recognition in judicial domain: A multilayer support vector machines approach. In Proceeding of the 6th International Conference on Machine Learning and Data Mining in Pattern Recognition (pp.594-602) [10.1007/978-3-642-03070-3_45].

Audio-based emotion recognition in judicial domain: A multilayer support vector machines approach

FERSINI, ELISABETTA;MESSINA, VINCENZINA;ARCHETTI, FRANCESCO ANTONIO
2009

Abstract

Thanks to the recent progresses in judicial proceedings management, especially related to the introduction of audio/video recording systems, semantic retrieval is a key challenge. In this context emotion recognition engine, through the analysis of vocal signature of actors involved in judicial proceedings, could provide useful annotations for semantic retrieval of multimedia clips. With respect to the generation of semantic emotional tag in judicial domain, two main contributions are given: (1) the construction of an Italian emotional database for Italian proceedings annotation; (2) the investigation of a hierarchical classification system, based on risk minimization method, able to recognize emotional states from vocal signatures. In order to estimate the degree of affection we compared the proposed classification method with SVM, K-Nearest Neighbors and Naive Bayes, highlighting in terms of classification accuracy, the improvements given by a hierarchical learning approach. © 2009 Springer Berlin Heidelberg.
No
paper
SVM; emotion recognition
English
Machine Learning and Data Mining in Pattern Recognition
978-3-642-03069-7
Fersini, E., Messina, V., Arosio, G., Archetti, F. (2009). Audio-based emotion recognition in judicial domain: A multilayer support vector machines approach. In Proceeding of the 6th International Conference on Machine Learning and Data Mining in Pattern Recognition (pp.594-602) [10.1007/978-3-642-03070-3_45].
Fersini, E; Messina, V; Arosio, G; Archetti, F
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/10281/8916
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