An Incremental Turn-Taking Model with Active System Barge-in for Spoken Dialog Systems
Tiancheng Zhao, Alan W. Black, Maxine Eskénazi · 2015
This paper deals with an incremental turntaking model that provides a novel solution for end-of-turn detection.It includes a flexible framework that enables active system barge-in.In order to accomplish this, a systematic procedure of teaching a dialog system to produce meaningful system barge-in is presented.This procedure improves system robustness and success rate.It includes constructing cost models and learning optimal policy using reinforcement learning.Results show that our model reduces false cut-in rate by 37.1% and response delay by 32.5% compared to the baseline system.Also the learned system barge-in strategy yields a 27.7% increase in average reward from user responses.