Efficient use of the grammar scale factor to classify incorrect words in speech recognition verification

The goal of verification in speech recognition systems is to detect words in the hypothesized sentence that are likely to have been misrecognized. This decision can be based on the persistence of the different words in the output of the speech recognizer when some recognition parameter is varied. To this end, a parameter that proves particularly adequate is the so called grammar scale factor (which balances acoustic and language model scores). The main disadvantage of this method is that it needs to repeat the recognition process many times. In the paper, after formulating it as a statistical pattern classification problem, we show how to speed-up this method, so that less than two average repetitions of the recognition process are enough to achieve essentially the same verification performance as with the many more repetitions needed by the original proposal