CRF-based confidence measures of recognized candidates for lattice-based audio indexing

The use of forward-backward (FB) computation based posterior probabilities as confidence measures (CMs) for all recognized candidates in a lattice seems to be common across various lattice-based audio indexing systems. However, a major limitation with this approach is that its performance for CMs cannot be improved easily, since it relies almost entirely on a single information source - the acoustic and language-model probabilities. In this paper, we propose to formulate computing CMs in the lattice case as a multi-class sequential labeling problem, using conditional random fields (CRFs) as the underlying model. In this approach, various relevant features including the FB posterior probabilities could be combined together. Note that CRFs are well suited to label sequence data and some features are defined over a word sequence. This paper presents how we resolve these two issues in the lattice case, beyond others´ previous work in CRF-based CMs for the 1-best case. Once properly implemented, the proposed approach achieves significant performance improvements for both CMs in the lattice case and lattice-based audio indexing.