Word-based acoustic confidence measures for large-vocabulary speech recognition

Word level confidence measures are of use in many areas of speech recognition. Comparing the hypothesized word score to the score of a ‘filler’ model has been the most popular confidence measure because it is highly efficient, and does not require a large amount of training data. This paper explores an extension of this technique which also compares the hypothesized word score to the scores of words that are commonly confused for it, while maintaining efficiency and the low demand for training data. The proposed method gives a 39% relative false accept rate reduction over the ‘filler’model baseline, at a false reject rate of 5%.