A Voice Dialog System without Interfering with Human Speech Based on Turn-taking Detection

In this study, we developed a new rule-based dialog system that includes both turn-taking detection and response-switching functions. The traditional rule-based part allows the system user to design and customize topics and conversation transitions easily. In addition to the traditional rule-based part, the turn-taking detection function can detect the human speaker's continuous speech and control the system's speech response according to the detected continuous speech. This mechanism enables conversations without interfering with the speech of the human speaker. The turn-taking detection was created using a deep neural network (DNN). Using the turn-taking detection result, the response switching function can change three types of response transitions: a back-channeling response, topic changing, and normal conversation transition. To confirm that our developed functions could improve the impressions of rule-based dialog systems, an experiment comparing the dialog system with and without our developed functions was conducted. The system with both turn-taking detection and response switching functions provided a relatively better impression to the participants. Although some improvements are needed for the dialog system, the results suggest that the use of a turn-taking detection function may enable a relatively smooth conversation even with a rule-based dialog system.