In this paper, we report on classification results for emotional user states (4 classes, German database of children interacting with a pet robot). Starting with 5 emotion labels per word, we obtained chunks with different degrees of prototypicality. Six sites computed acoustic and linguistic features independently from each other. A total of 4232 features were pooled together and grouped into 10 low level descriptor types. For each of these groups separately and for all taken together, classification results using Support Vector Machines are reported for 150 features each with the highest individual Information Gain Ratio, for a scale of prototypicality. With both acoustic and linguistic features, we obtained a relative improvement of up to 27.6%, going from low to higher prototypicality.
Patterns, Prototypes, Performance: Classifying Emotional User States
Seppi, Dino
2008-01-01
Abstract
In this paper, we report on classification results for emotional user states (4 classes, German database of children interacting with a pet robot). Starting with 5 emotion labels per word, we obtained chunks with different degrees of prototypicality. Six sites computed acoustic and linguistic features independently from each other. A total of 4232 features were pooled together and grouped into 10 low level descriptor types. For each of these groups separately and for all taken together, classification results using Support Vector Machines are reported for 150 features each with the highest individual Information Gain Ratio, for a scale of prototypicality. With both acoustic and linguistic features, we obtained a relative improvement of up to 27.6%, going from low to higher prototypicality.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
