A Data-driven Model for Timing Feedback in a Map Task Dialogue System
| Raveesh Meena, Gabriel Skantze, Joakim Gustafson |
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We present a data-driven model for detecting suitable response locations in the user's speech. The model has been trained on humanâmachine dialogue data and implemented and tested in a spoken dialogue system that can perform the Map Task with users. To our knowledge, this is the first example of a dialogue system that uses automatically extracted syntactic, prosodic and contextual features for online detection of response locations. A subjective evaluation of the dialogue system suggests that interactions with a system using our trained model were perceived significantly better than those with a system using a model that made decisions at random.
