Affective computing
Data mining: practical machine learning tools and techniques with Java implementations
Data mining: practical machine learning tools and techniques with Java implementations
Robots for kids: exploring new technologies for learning
Robots for kids: exploring new technologies for learning
Designing Sociable Robots
Vocal communication of emotion: a review of research paradigms
Speech Communication - Special issue on speech and emotion
The production and recognition of emotions in speech: features and algorithms
International Journal of Human-Computer Studies - Application of affective computing in humanComputer interaction
Voice-based gender identification in multimedia applications
Journal of Intelligent Information Systems - Special issue: Intelligent multimedia applications
Pattern Recognition and Machine Learning (Information Science and Statistics)
Pattern Recognition and Machine Learning (Information Science and Statistics)
Two-stage Classification of Emotional Speech
ICDT '06 Proceedings of the international conference on Digital Telecommunications
Emotion detection in task-oriented spoken dialogues
ICME '03 Proceedings of the 2003 International Conference on Multimedia and Expo - Volume 3 (ICME '03) - Volume 03
Voting ensembles for spoken affect classification
Journal of Network and Computer Applications
Automatic Hierarchical Classification of Emotional Speech
ISMW '07 Proceedings of the Ninth IEEE International Symposium on Multimedia Workshops
Extracting emotions from music data
ISMIS'05 Proceedings of the 15th international conference on Foundations of Intelligent Systems
ANN application in emotional speech analysis
International Journal of Data Analysis Techniques and Strategies
Dimensionality reduction-based spoken emotion recognition
Multimedia Tools and Applications
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This paper deals with speech emotion analysis within the context of increasing awareness of the wide application potential of affective computing. Unlike most works in the literature which mainly rely on classical frequency and energy based features along with a single global classifier for emotion recognition, we propose in this paper some new harmonic and Zipf based features for better speech emotion characterization in the valence dimension and a multi-stage classification scheme driven by a dimensional emotion model for better emotional class discrimination. Experimented on the Berlin dataset with 68 features and six emotion states, our approach shows its effectiveness, displaying a 68.60% classification rate and reaching a 71.52% classification rate when a gender classification is first applied. Using the DES dataset with five emotion states, our approach achieves an 81% recognition rate when the best performance in the literature to our knowledge is 76.15% on the same dataset.