Industrial Applications of Fuzzy Control
Industrial Applications of Fuzzy Control
Learning and Soft Computing: Support Vector Machines, Neural Networks, and Fuzzy Logic Models
Learning and Soft Computing: Support Vector Machines, Neural Networks, and Fuzzy Logic Models
Describing the emotional states that are expressed in speech
Speech Communication - Special issue on speech and emotion
Emotional speech: towards a new generation of databases
Speech Communication - Special issue on speech and emotion
How to find trouble in communication
Speech Communication - Special issue on speech and emotion
Vocal communication of emotion: a review of research paradigms
Speech Communication - Special issue on speech and emotion
Indirect acquisition of instrumental gesture based on signal, physical and perceptual information
NIME '03 Proceedings of the 2003 conference on New interfaces for musical expression
2005 Special Issue: Challenges in real-life emotion annotation and machine learning based detection
Neural Networks - Special issue: Emotion and brain
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
Toward a rule-based synthesis of emotional speech on linguistic descriptions of perception
ACII'05 Proceedings of the First international conference on Affective Computing and Intelligent Interaction
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This paper proposes a multi-layer approach to modeling perception of expressive speech. Many earlier studies of expressive speech focused on statistical correlations between expressive speech and acoustic features without taking into account the fact that human perception is vague rather than precise. This paper introduces a three-layer model: five categories of expressive speech constitute the top layer, semantic primitives constitute the middle layer, and acoustic features, the bottom layer. Three experiments followed by multidimensional scaling analysis revealed suitable semantic primitives. Then, fuzzy inference systems were built to map the vagueness of the relationship between expressive speech and the semantic primitives. Acoustic features in terms of F0 contour, time duration, power envelope, and spectrum were analyzed. Regression analysis revealed correlation between the semantic primitives and the acoustic features. Parameterized rules based on the analysis results were created to morph neutral utterances to those perceived as having different semantic primitives and expressive speech categories. Experiments to verify the relationships of the model showed significant relationships between expressive speech, semantic primitives, and acoustic features.