Recent works have proposed neural models for dialog act classification in spoken dialogs.
However, they have not explored the role and the usefulness of acoustic information.
We propose a neural model that processes both lexical and acoustic features for classification.
Our results on two benchmark datasets reveal that acoustic features are helpful in improving the overall accuracy.
Finally, a deeper analysis shows that acoustic features are valuable in three cases: when a dialog act has sufficient data, when lexical information is limited and when strong lexical cues are not present.