Term-weighting approaches in automatic text retrieval
Information Processing and Management: an International Journal
The nature of statistical learning theory
The nature of statistical learning theory
Text Categorization with Suport Vector Machines: Learning with Many Relevant Features
ECML '98 Proceedings of the 10th European Conference on Machine Learning
Describing the emotional states that are expressed in speech
Speech Communication - Special issue on speech and emotion
A tutorial on support vector regression
Statistics and Computing
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
Spatial Presence and Emotions during Video Game Playing: Does It Matter with Whom You Play?
Presence: Teleoperators and Virtual Environments
Primitives-based evaluation and estimation of emotions in speech
Speech Communication
Automatic Recognition of Spontaneous Emotions in Speech Using Acoustic and Lexical Features
MLMI '08 Proceedings of the 5th international workshop on Machine Learning for Multimodal Interaction
A dimensional approach to emotion recognition of speech from movies
ICASSP '09 Proceedings of the 2009 IEEE International Conference on Acoustics, Speech and Signal Processing
IEEE Transactions on Affective Computing
Introducing EVG: an emotion evoking game
IVA'06 Proceedings of the 6th international conference on Intelligent Virtual Agents
Affective video content representation and modeling
IEEE Transactions on Multimedia
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The differences between self-reported and observed emotion have only marginally been investigated in the context of speech-based automatic emotion recognition. We address this issue by comparing self-reported emotion ratings to observed emotion ratings and look at how differences between these two types of ratings affect the development and performance of automatic emotion recognizers developed with these ratings. A dimensional approach to emotion modeling is adopted: the ratings are based on continuous arousal and valence scales. We describe the TNO-Gaming Corpus that contains spontaneous vocal and facial expressions elicited via a multiplayer videogame and that includes emotion annotations obtained via self-report and observation by outside observers. Comparisons show that there are discrepancies between self-reported and observed emotion ratings which are also reflected in the performance of the emotion recognizers developed. Using Support Vector Regression in combination with acoustic and textual features, recognizers of arousal and valence are developed that can predict points in a 2-dimensional arousal-valence space. The results of these recognizers show that the self-reported emotion is much harder to recognize than the observed emotion, and that averaging ratings from multiple observers improves performance.