A data-driven approach to spoken dialog segmentation

In this paper, we present a statistical model for spoken dialog segmentation that decides the current phase of the dialog by means of an automatic classification process. We have applied our proposal to three practical conversational systems acting in different domains. The results of the evaluation show that is possible to attain high accuracy rates in dialog segmentation when using different sources of information to represent the user input. Our results indicate how the module proposed can also improve dialog management by selecting better system answers. The statistical model developed with human-machine dialog corpora has been applied in one of our experiments to human-human conversations and provides a good baseline as well as insights in the model limitation. © 2019 Elsevier B.V.