The nature of statistical learning theory
The nature of statistical learning theory
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
2005 Special Issue: Challenges in real-life emotion annotation and machine learning based detection
Neural Networks - Special issue: Emotion and brain
ACII'05 Proceedings of the First international conference on Affective Computing and Intelligent Interaction
Primitives-based evaluation and estimation of emotions in speech
Speech Communication
Real-Life Emotion Recognition in Speech
Speaker Classification II
On the Use of Kappa Coefficients to Measure the Reliability of the Annotation of Non-acted Emotions
PIT '08 Proceedings of the 4th IEEE tutorial and research workshop on Perception and Interactive Technologies for Speech-Based Systems: Perception in Multimodal Dialogue Systems
Representing Emotions with Linguistic Acuity
CICLing '07 Proceedings of the 8th International Conference on Computational Linguistics and Intelligent Text Processing
Emotion recognition from speech: a review
International Journal of Speech Technology
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Since the early studies of human behavior, emotions have attracted the interest of researchers in Neuroscience and Psychology. Recently, it has been a growing field of research in computer science. We are exploring how to represent and automatically detect a subject’s emotional state. In contrast with most previous studies conducted on artificial data, this paper addresses some of the challenges faced when studying real-life non-basic emotions. Real-life spoken dialogs from call-center services have revealed the presence of many blended emotions. A soft emotion vector is used to represent emotion mixtures. This representation enables to obtain a much more reliable annotation and to select the part of the corpus without conflictual blended emotions for training models. A correct detection rate of about 80% is obtained between Negative and Neutral emotions and between Fear and Neutral emotions using paralinguistic cues on a corpus of 20 hours of recording.