Reinforcement Learning-based Spoken Dialog Strategy Design for In-Vehicle Speaking Assistant

Abstract

In this paper, the simulated annealing Q-learning (SA-Q) algorithm is adopted to automatically learn the optimal dialogue strategy of a spoken dialogue system. Several simulations and experiments considering different user behaviors and speech recognizer performance are conducted to verify the effectiveness of the SA-Q learning approach. Moreover, the automatically learned strategy is applied to an in-vehicle speaking assistant prototype system with real user response inputs to enable a driver to easily control various in-car equipments including a GPS-based navigation system. Key Word: reinforcement learning, Q-learning, dialogue strategy, spoken dialog system

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