The nature of statistical learning theory
The nature of statistical learning theory
Fast training of support vector machines using sequential minimal optimization
Advances in kernel methods
A Comparative Study on Feature Selection in Text Categorization
ICML '97 Proceedings of the Fourteenth International Conference on Machine Learning
The role of voice quality in communicating emotion, mood and attitude
Speech Communication - Special issue on speech and emotion
Emotion recognition from speech: a review
International Journal of Speech Technology
Emotion recognition from speech using source, system, and prosodic features
International Journal of Speech Technology
Emotion recognition from speech using global and local prosodic features
International Journal of Speech Technology
Dimensionality reduction-based spoken emotion recognition
Multimedia Tools and Applications
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Nowadays, recognition of human emotion is a challenging yet important speech technology. In this paper, based on deriving prosody features from emotional speech, some voice quality features are proposed to be extracted as new emotional features to improve emotion recognition. Utilizing support vector machines classifier, four emotions from Chinese natural emotional speech corpus including anger, joy, sadness and neutral are discriminated by combining prosody and voice quality features. The experiment results show that combining prosody and voice quality features yields an overall accuracy of 76% for emotion recognition, which makes approximately 10% improvement compared with using the single prosody features. It also shows that voice quality features in speech are effective emotional features and can promote prosody features for improving emotion recognition results.