Abstract
Dialogue Management (DM) is an essential issue in Spoken Dialogue Systems (SDS). Most of previous studies on DM do not consider the visual feedback from machine to user that could accelerate the dialogue process dramatically. Thus, in this paper, we firstly model the DM problem in SDS with visual feedback as Partially Observable Markov Decision Processes (POMDP). Additionally, Reinforcement Learning (RL) approach is utilized to solve this problem, which yields the Vision and Audition-based DM (VADM) scheme. Finally, extensive experiment results illustrate the performance improvements of the proposed VADM scheme over the existing scheme in different scenarios.
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