Introduction to statistical pattern recognition (2nd ed.)
Introduction to statistical pattern recognition (2nd ed.)
Recognition of Affective Communicative Intent in Robot-Directed Speech
Autonomous Robots
Baby ears: a recognition system for affective vocalizations
Speech Communication
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
The production and recognition of emotions in speech: features and algorithms
International Journal of Human-Computer Studies - Application of affective computing in humanComputer interaction
2005 Special Issue: Challenges in real-life emotion annotation and machine learning based detection
Neural Networks - Special issue: Emotion and brain
ASR for emotional speech: Clarifying the issues and enhancing performance
Neural Networks - Special issue: Emotion and brain
Ensemble methods for spoken emotion recognition in call-centres
Speech Communication
Primitives-based evaluation and estimation of emotions in speech
Speech Communication
Applying an analysis of acted vocal emotions to improve the simulation of synthetic speech
Computer Speech and Language
Audiovisual recognition of spontaneous interest within conversations
Proceedings of the 9th international conference on Multimodal interfaces
User Modeling and User-Adapted Interaction
Time- and Amplitude-Based Voice Source Correlates of Emotional Portrayals
ACII '07 Proceedings of the 2nd international conference on Affective Computing and Intelligent Interaction
ACII '07 Proceedings of the 2nd international conference on Affective Computing and Intelligent Interaction
A Survey of Affect Recognition Methods: Audio, Visual, and Spontaneous Expressions
IEEE Transactions on Pattern Analysis and Machine Intelligence
Detecting changing emotions in natural speech
IEA/AIE'12 Proceedings of the 25th international conference on Industrial Engineering and Other Applications of Applied Intelligent Systems: advanced research in applied artificial intelligence
Vocal markers of emotion: Comparing induction and acting elicitation
Computer Speech and Language
Detecting changing emotions in human speech by machine and humans
Applied Intelligence
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The majority of previous studies on vocal expression have been conducted on posed expressions. In contrast, we utilized a large corpus of authentic affective speech recorded from real-life voice controlled telephone services. Listeners rated a selection of 200 utterances from this corpus with regard to level of perceived irritation, resignation, neutrality, and emotion intensity. The selected utterances came from 64 different speakers who each provided both neutral and affective stimuli. All utterances were further automatically analyzed regarding a comprehensive set of acoustic measures related to F0, intensity, formants, voice source, and temporal characteristics of speech. Results first showed that several significant acoustic differences were found between utterances classified as neutral and utterances classified as irritated or resigned using a within-persons design. Second, listeners' ratings on each scale were associated with several acoustic measures. In general the acoustic correlates of irritation, resignation, and emotion intensity were similar to previous findings obtained with posed expressions, though the effect sizes were smaller for the authentic expressions. Third, automatic classification (using LDA classifiers both with and without speaker adaptation) of irritation, resignation, and neutral performed at a level comparable to human performance, though human listeners and machines did not necessarily classify individual utterances similarly. Fourth, clearly perceived exemplars of irritation and resignation were rare in our corpus. These findings were discussed in relation to future research.