How to find trouble in communication
Speech Communication - Special issue on speech and emotion
Data Mining: Practical Machine Learning Tools and Techniques, Second Edition (Morgan Kaufmann Series in Data Management Systems)
User Modeling and User-Adapted Interaction
Comparison of Different Classifiers for Emotion Recognition
PCI '09 Proceedings of the 2009 13th Panhellenic Conference on Informatics
NOLISP'11 Proceedings of the 5th international conference on Advances in nonlinear speech processing
Affective speech interface in serious games for supporting therapy of mental disorders
Expert Systems with Applications: An International Journal
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In the present work we aim at performance optimization of a speaker-independent emotion recognition system through speech feature selection process. Specifically, relying on the speech feature set defined in the Interspeech 2009 Emotion Challenge, we studied the relative importance of the individual speech parameters, and based on their ranking, a subset of speech parameters that offered advantageous performance was selected. The affect-emotion recognizer utilized here relies on a GMM-UBM-based classifier. In all experiments, we followed the experimental setup defined by the Interspeech 2009 Emotion Challenge, utilizing the FAU Aibo Emotion Corpus of spontaneous, emotionally coloured speech. The experimental results indicate that the correct choice of the speech parameters can lead to better performance than the baseline one.