A comparison using different speech parameters in the automatic emotion recognition using feature subset selection based on evolutionary algorithms

Authors:
Aitor Álvarez;Idoia Cearreta;Juan Miguel López;Andoni Arruti;Elena Lazkano;Basilio Sierra;Nestor Garay
Affiliations:
Computer Science Faculty, University of the Basque Country, Gipuzkoa, Spain;Computer Science Faculty, University of the Basque Country, Donostia, Gipuzkoa, Spain;Computer Science Faculty, University of the Basque Country, Donostia, Gipuzkoa, Spain;Computer Science Faculty, University of the Basque Country, Donostia, Gipuzkoa, Spain;Computer Science Faculty, University of the Basque Country, Donostia, Gipuzkoa, Spain;Computer Science Faculty, University of the Basque Country, Donostia, Gipuzkoa, Spain;Computer Science Faculty, University of the Basque Country, Donostia, Gipuzkoa, Spain
Venue:
TSD'07 Proceedings of the 10th international conference on Text, speech and dialogue
Year:
2007

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Abstract

Study of emotions in human-computer interaction is a growing research area. Focusing on automatic emotion recognition, work is being performed in order to achieve good results particularly in speech and facial gesture recognition. This paper presents a study where, using a wide range of speech parameters, improvement in emotion recognition rates is analyzed. Using an emotional multimodal bilingual database for Spanish and Basque, emotion recognition rates in speech have significantly improved for both languages comparing with previous studies. In this particular case, as in previous studies, machine learning techniques based on evolutive algorithms (EDA) have proven to be the best emotion recognition rate optimizers.