Optimum recognition parameters in multiple domains

Abstract

We evaluated large-vocabulary continuous-speech recognizer performance as a function of recognizer tuning parameters for 4 recognition task domains (location, date, time, yes/no) and two different applications (e.g. over-the-telephone reservations) that had some task domains in common. After defining a cost function that included false reject, false accept, and misrecognition errors, we determined optimum parameter values for each domain. The optimum parameter settings differed significantly across domains and even across applications for the same domain. Using a single set of parameter values for all of the tasks in an application can lead to substantial cost penalties for some individual tasks. These results suggest that there can be substantial benefit in using task-specific tuned recognition parameters. We describe a methodology and set of supporting tools for efficiently performing taskspecific tuning.