Abstract
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.
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Notes
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- 2.
In all our experiments we keep 80%/10%/10% training/validation/test split.
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TypedFlow: https://github.com/GU-CLASP/TypedFlow.
- 4.
Models and data are available at: https://github.com/GU-CLASP/laughter-spaces.
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Maraev, V., Howes, C., Bernardy, JP. (2021). Predicting Laughter Relevance Spaces in Dialogue. In: Marchi, E., Siniscalchi, S.M., Cumani, S., Salerno, V.M., Li, H. (eds) Increasing Naturalness and Flexibility in Spoken Dialogue Interaction. Lecture Notes in Electrical Engineering, vol 714. Springer, Singapore. https://doi.org/10.1007/978-981-15-9323-9_4
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