Interactive Spoken Content Retrieval with Different Types of Actions Optimized by a Markov Decision Process

摘要

Interaction with user is specially important for spoken content retrieval, not only because of the recognition uncertainty, but because the retrieved spoken content items are difficult to be shown on the screen and difficult to be scanned and selected by the user. The user cannot playback and go through all the retrieved items and then find out they are not what he is looking for. In this paper, we propose a new approach for interactive spoken content retrieval, in which the system can estimate the quality of the retrieved results, and take different types of actions to clarify the user's intention based on an intrinsic policy. The policy is optimized by a Markov Decision Process (MDP) trained with Reinforcement Learning based on a set of pre-defined rewards considering the extra burden given to the user.