2005 Special Issue: Challenges in real-life emotion annotation and machine learning based detection
Neural Networks - Special issue: Emotion and brain
Humor: prosody analysis and automatic recognition for F*R*I*E*N*D*S*
EMNLP '06 Proceedings of the 2006 Conference on Empirical Methods in Natural Language Processing
Fear-type emotion recognition for future audio-based surveillance systems
Speech Communication
Exploration of Affect Sensing from Speech and Metaphorical Text
Edutainment '09 Proceedings of the 4th International Conference on E-Learning and Games: Learning by Playing. Game-based Education System Design and Development
Fiction support for realistic portrayals of fear-type emotional manifestations
Computer Speech and Language
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In this study we present a novel emotional speech corpus, consisting of dialog that was extracted from an animated film. This type of corpus presents an interesting compromise between the sparsity of emotion found in spontaneous speech, and the contrived emotion found in speech acted solely for research purposes. The dialog was segmented into 453 short units and judged for emotional content by native and non-native English speakers. Emotion was rated on two scales: Activation and Valence. Acoustic analysis gave a comprehensive set of 100 features covering F0, intensity, voice quality and spectrum. We found that Activation is more strongly correlated to our acoustic features than Valence. Activat-ion was correlated to several types of features, whereas Valence was correlated mainly to intensity related features. Further, ANOVA analysis showed some interesting contrasts between the two scales, and interesting differences in the judgments of native vs. non-native English speakers.