LAUGHTER DETECTION FOR ON-LINE HUMAN-ROBOT INTERACTION
Résumé
This paper presents a study of laugh classification using a cross-corpus protocol. It aims at the automatic detection of laughs in a real-time human-machine interaction. Positive and negative laughs are tested with different classification tasks and different acoustic feature sets. F.measure results show an improvement on positive laughs classification from 59.5% to 64.5% and negative laughs recognition from 10.3% to 28.5%. In the context of the Chist-Era JOKER project, positive and negative laugh detection drives the policies of the robot Nao. A measure of engagement will be provided using also the number of positive laughs detected during the interaction.
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