  Conversational engagement estimation is posed as a regression problem,
entailing the identification of the favorable attention and involvement of the
participants in the conversation. This task arises as a crucial pursuit to gain
insights into human's interaction dynamics and behavior patterns within a
conversation. In this research, we introduce a dilated convolutional
Transformer for modeling and estimating human engagement in the MULTIMEDIATE
2023 competition. Our proposed system surpasses the baseline models, exhibiting
a noteworthy $7$\% improvement on test set and $4$\% on validation set.
Moreover, we employ different modality fusion mechanism and show that for this
type of data, a simple concatenated method with self-attention fusion gains the
best performance.

Multimedia;Computation and Language;Machine Learning;Sound;Audio and Speech Processing