Estimating Confidence Measures for Speech Recognition Verification Using a~Smoothed Naive Bayes Model

Verification in speech recognition systems can be seen as a conventional pattern classification problem in which each hypothesized word is to be transformed into a feature vector and then classified as either correct or incorrect. Thus, our basic problems are to find appropriate pattern features and to design an accurate pattern classifier. In this paper, we present a new feature and a smoothed naive Bayes classification model. Experimental results are reported comparing the new feature with a set of well-known features. The best performance is obtained using the new feature in combination with Acoustic Stability.