CorpVis: An Online Emotional Speech Corpora Visualisation Interface
SAMT '09 Proceedings of the 4th International Conference on Semantic and Digital Media Technologies: Semantic Multimedia
Computer Speech and Language
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Computer Speech and Language
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Using affective parameters in a content-based recommender system for images
User Modeling and User-Adapted Interaction
Enhancing emotion recognition from speech through feature selection
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User Modeling and User-Adapted Interaction
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Advances in Human-Computer Interaction - Special issue on emotion-aware natural interaction
Affect recognition in real life scenarios
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On the impact of children's emotional speech on acoustic and language models
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Speech Communication
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Expert Systems with Applications: An International Journal
Paralinguistics in speech and language-State-of-the-art and the challenge
Computer Speech and Language
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Computer Speech and Language
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International Journal of Distance Education Technologies
Computer Speech and Language
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The `traditional' first two dimensions in emotion research are VALENCE and AROUSAL. Normally, they are obtained by using elicited, acted data. In this paper, we use realistic, spontaneous speech data from our `AIBO' corpus (human-robot communication, children interacting with Sony's AIBO robot). The recordings were done in a Wizard-of-Oz scenario: the children believed that AIBO obeys their commands; in fact, AIBO followed a fixed script and often disobeyed. Five labellers annotated each word as belonging to one of eleven emotion-related states; seven of these states which occurred frequently enough are dealt with in this paper. The confusion matrices of these labels were used in a Non-Metrical Multi-dimensional Scaling to display two dimensions; the first we interpret as VALENCE, the second, however, not as AROUSAL but as INTERACTION, i.e., addressing oneself (angry, joyful) or the communication partner (motherese, reprimanding). We show that it depends on the specifity of the scenario and on the subjects' conceptualizations whether this new dimension can be observed, and discuss impacts on the practice of labelling and processing emotional data. Two-dimensional solutions based on acoustic and linguistic features that were used for automatic classification of these emotional states are interpreted along the same lines.