A comparison of word graph and n-best list based confidence measures
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Abstract
In this paper we present and compare several confidence measures for large vocabulary continuous speech recognition.We show that posterior word probabilities computed on word graphs and N-best lists clearly outperform non-probabilistic confidence measures, e.g. the acoustic stability and the hypothesis density.In addition, we prove that the estimation of posterior word probabilities on word graphs yields better results than their estimation on N-best lists and discuss both methods in detail.We present experimental results on three different corpora, the English NAB '94 20k development corpus, the German VERBMOBIL '96 evaluation corpus and a Dutch corpus, which has been recorded with a train timetable information system in the ARISE project.
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