Improved lattice-based spoken document retrieval by directly learning from the evaluation measures

Lattice-based approaches have been widely used in spoken document retrieval to handle the speech recognition uncertainty and errors. Position Specific Posterior Lattices (PSPL) and Confusion Network (CN) are good examples. It is therefore interesting to derive improved model for spoken document retrieval by properly integrating different versions of lattice-based approaches in order to achieve better performance. In this paper we borrow the framework of dasialearning to rankpsila from text document retrieval and try to integrate it into the scenario of lattice-based spoken document retrieval. Two approaches are considered here, AdaRank and SVM-map. With these approaches, we are able to learn and derived improved models using different versions of PSPL/CN. Preliminary experiments with broadcast news in Mandarin Chinese showed significant improvements.