Random Forests-Based Confidence Annotation Using Novel Features from Confusion Network

In this paper, we propose a set of new features for confidence annotation, including three features derived from confusion network and one from statistical significance test. We also propose using random forests as confidence classifier. The new features are combined with a set of eight previously proposed confidence features, and the random forests is compared with decision tree and support vector machine. Experiments were conducted on telehealth captioning task with a vocabulary size of 46,489. Average confidence annotation accuracy of 84.69% was achieved on 5 doctors´ test set. In addition, random forests was shown useful for feature importance ranking. The proposed features are shown important in confidence annotation and random forests achieved best results among the three classifiers