Confidence measure by substring comparison for automatic speech recognition

Abstract

Two possible confidence measures for automatic speech recognition are presented along with results of tests where they were applied. One of them is widely known and it is based on comparing the strongest hypotheses with an average of a few next hypotheses. We found it not efficient in all cases, this is why we came up with our own method based on comparison of substrings. New algorithm was found useful in real applications for spoken dialogue system, in a module asking to repeat a phrase or declaring that it was not recognised. The method was designed for Polish language, which is morphologically rich. The method is tuned to situations in which there are several similar utterances in a dictionary.