Using word confidence measure for OOV words detection in a spontaneous spoken dialog system

Abstract

Developing a real-life spoken dialogue system must face with many practical issues, where the out-of-vocabulary (OOV) words problem is one of the key difficulties. This paper presents the OOV detection mechanism based on the word confidence scoring developed for the d-Ear Attendant system, a spontaneous spoken dialogue system. In the d-Ear Attendant system, an explicit filler model is originally used to detect the presence of OOV words [1]. Although this approach has a satisfactory OOV detection rate, it badly degrades the accuracy of in-vocabulary (IV) detection by 4.4% absolutely (from 97% to 92.6%). Such the degradation will not be acceptable in a practical system. By using a few commonly used acoustic confidence features and some new context confidence features, our confidence measure method not only is able to detect the word level speech recognition errors, but also has a good ability for OOV words detection with an acceptable false alarm rate. For example, with a false rejection rate of 2.5%, the false acceptance rate of 26% is achieved.