Predicting and Adapting to Poor Speech Recognition in a Spoken Dialogue System

Abstract

Spoken dialogue system performance can vary widely for different users, as well for the same user during different dialogues. This paper presents the design and evaluation of an adaptive version of TOOT, a spoken dialogue system for retrieving online train schedules. Adaptive TOOT predicts whether a user is having speech recognition problems as a particular dialogue progresses, and automatically adapts its dialogue strategies based on its predictions. An empirical evaluation of the system demonstrates the utility of the approach. Introduction Most spoken dialogue systems do not try to improve performance by dynamically adapting the system's dialogue behaviors to an individual user during the course of a particular dialogue. But the performance of a spoken dialogue system can vary significantly for different users and even for the same user across dialogues. This paper presents the design and experimental evaluation of a spoken dialogue system that predicts and responds to pr...