Predicting laughter relevance spaces

In this paper we address the task of predicting spaces in interaction where laughter can occur. We introduce the new task of predicting actual laughs in dialogue and address it with various deep learning models, namely recurrent neural network (RNN), convolution neural network (CNN) and combinations of these. We also attempt to evaluate human performance for this task via an Amazon Mechanical Turk (AMT) experiment. The main finding of the present work is that deep learning models outperform untrained humans in this task.
Research areas:
Type of Publication:
In Proceedings
Book title:
Proceedings of the International Workshop on Spoken Dialog System Technology
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