Spoken dialog systems typically use a limited number of nonunderstanding recovery strategies and simple heuristic policies 1 to engage them (e.g. first ask user to repeat, then give help, then transfer to an operator). We propose a supervised, online method for learning a non-understanding recovery policy over a large set of recovery strategies. The approach consists of two steps: first, we construct runtime estimates for the likelihood of success of each recovery strategy, and then we use these estimates to construct a policy. An experiment with a publicly available spoken dialog system shows that the learned policy produced a 12.5% relative improvement in the non-understanding recovery rate.
This paper describes work designed to improve understandability of spoken output, specifically for the elderly, by using a speaking style employed by people to improve their understandability when speaking in poor channel conditions. We describe an experiment that shows the understandability gains that are possible using naturally-produced examples of this style. Additionally, we describe how to model this style, and evaluate the differences in understandability for speech synthesis produced using those models.
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