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.
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Álvarez, A. et al. (2007). A Comparison Using Different Speech Parameters in the Automatic Emotion Recognition Using Feature Subset Selection Based on Evolutionary Algorithms. In: Matoušek, V., Mautner, P. (eds) Text, Speech and Dialogue. TSD 2007. Lecture Notes in Computer Science(), vol 4629. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-74628-7_55
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DOI: https://doi.org/10.1007/978-3-540-74628-7_55
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