Affective computing
Data mining: practical machine learning tools and techniques with Java implementations
Data mining: practical machine learning tools and techniques with Java implementations
Recognition of Affective Communicative Intent in Robot-Directed Speech
Autonomous Robots
Baby ears: a recognition system for affective vocalizations
Speech Communication
How to find trouble in communication
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
Computer Speech and Language
EmoReSp: an online emotion recognizer based on speech
Proceedings of the 11th International Conference on Computer Systems and Technologies and Workshop for PhD Students in Computing on International Conference on Computer Systems and Technologies
Advances in Human-Computer Interaction - Special issue on emotion-aware natural interaction
Emotion recognition from speech by combining databases and fusion of classifiers
TSD'10 Proceedings of the 13th international conference on Text, speech and dialogue
ANN application in emotional speech analysis
International Journal of Data Analysis Techniques and Strategies
Paralinguistics in speech and language-State-of-the-art and the challenge
Computer Speech and Language
Ten recent trends in computational paralinguistics
COST'11 Proceedings of the 2011 international conference on Cognitive Behavioural Systems
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We present a study on the automatic classification of expressiveness in speech using four databases that belong to two distinct groups: the first group of two databases contains adult speech directed to infants, while the second group contains adult speech directed to adults. We performed experiments with two approaches for feature extraction, the approach developed for Sony's robotic dog AIBO (AIBO) and a segment based approach (SBA), and three machine learning algorithms for training the classifiers. In mono corpus experiments, the classifiers were trained and tested on each database individually. The results show that AIBO and SBA are competitive on the four databases considered, although the AIBO approach works better with long utterances whereas the SBA seems to be better suited for classification of short utterances. When training was performed on one database and testing on another database of the same group, little generalization across the databases happened because emotions with the same label occupy different regions of the feature space for the different databases. Fortunately, when the databases are merged, classification results are comparable to within-database experiments, indicating that the existing approaches for the classification of emotions in speech are efficient enough to handle larger amounts of training data without any reduction in classification accuracy, which should lead to classifiers that are more robust to varying styles of expressiveness in speech.