The nature of statistical learning theory
The nature of statistical learning theory
International Journal of Human-Computer Studies - Special issue: Subtle expressivity for characters and robots
International Journal of Human-Computer Studies - Special issue: Subtle expressivity for characters and robots
Affective multimodal human-computer interaction
Proceedings of the 13th annual ACM international conference on Multimedia
Multimodal affect recognition in learning environments
Proceedings of the 13th annual ACM international conference on Multimedia
Automatic Hierarchical Classification of Emotional Speech
ISMW '07 Proceedings of the Ninth IEEE International Symposium on Multimedia Workshops
Interactive robots as social partners and peer tutors for children: a field trial
Human-Computer Interaction
SMOTE: synthetic minority over-sampling technique
Journal of Artificial Intelligence Research
Detecting emotional state of a child in a conversational computer game
Computer Speech and Language
Spoken emotion recognition using hierarchical classifiers
Computer Speech and Language
Analysis of Emotionally Salient Aspects of Fundamental Frequency for Emotion Detection
IEEE Transactions on Audio, Speech, and Language Processing
Classification of emotional speech using 3DEC hierarchical classifier
Speech Communication
Paralinguistics in speech and language-State-of-the-art and the challenge
Computer Speech and Language
Duration modeling for emotional speech
ICICA'12 Proceedings of the Third international conference on Information Computing and Applications
Hierarchical emotion classification using genetic algorithms
Proceedings of the Fourth Symposium on Information and Communication Technology
Computer Speech and Language
Human emotion recognition from videos using spatio-temporal and audio features
The Visual Computer: International Journal of Computer Graphics
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Automated emotion state tracking is a crucial element in the computational study of human communication behaviors. It is important to design robust and reliable emotion recognition systems that are suitable for real-world applications both to enhance analytical abilities to support human decision making and to design human-machine interfaces that facilitate efficient communication. We introduce a hierarchical computational structure to recognize emotions. The proposed structure maps an input speech utterance into one of the multiple emotion classes through subsequent layers of binary classifications. The key idea is that the levels in the tree are designed to solve the easiest classification tasks first, allowing us to mitigate error propagation. We evaluated the classification framework on two different emotional databases using acoustic features, the AIBO database and the USC IEMOCAP database. In the case of the AIBO database, we obtain a balanced recall on each of the individual emotion classes using this hierarchical structure. The performance measure of the average unweighted recall on the evaluation data set improves by 3.37% absolute (8.82% relative) over a Support Vector Machine baseline model. In the USC IEMOCAP database, we obtain an absolute improvement of 7.44% (14.58%) over a baseline Support Vector Machine modeling. The results demonstrate that the presented hierarchical approach is effective for classifying emotional utterances in multiple database contexts.