Towards a domain-independent ASR-confidence classifier

This work addresses the problem of developing a domain-independent binary classifier for a test domain given labeled data from several training domains where the test domain is not necessarily present in training data. The classifier accepts or rejects the ASR hypothesis based on the confidence generated by the ASR system. In the proposed approach, training data is grouped into across-domain clusters and separate cluster-specific classifiers are trained. One of the main findings is that the cluster purity and the normalized mutual information of the clusters are not very high which suggests that the domains might not necessarily be natural clusters. The performance of these cluster-specific classifiers is better than that of: (a) a single classifier trained on data from all the domains, and (b) a set of classifiers trained separately for each of the training domains. At an operating point corresponding to low False Accept, the Correct Accept of the proposed technique is on an average 2.3% higher than that obtained by the single-classifier or the individual train-domain classifiers.