Confidence measure and incremental adaptation for the rejection of incorrect data

This paper deals with the problem of incorrect data rejection in a large vocabulary directory task. Two different strategies are investigated to improve the rejection of noises and OOV data. An incremental adaptation algorithm is first proposed to adapt word models and a garbage model to field data. The second method consists in post-processing the recogniser hypotheses by computing for each of them a confidence measure based on frame level likelihood ratios. Both methods yield a noticeable reduction in the false alarm rate on noises and OOV data. Their combination leads to a further false alarm rate reduction