Lexico-acoustic Neural-based Models for Dialog Act Classification
This work addresses dialog act classification for spoken dialog systems, but it is incremental as it builds on existing neural models by adding acoustic features.
The paper tackled dialog act classification in spoken dialogs by proposing a neural model that integrates lexical and acoustic features, resulting in improved overall accuracy on two benchmark datasets.
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.