Combining Speech Retrieval Results with Generalized Additive Models

Rapid and inexpensive techniques for automatic transcription of speech have the potential to dramatically expand the types of content to which information retrieval techniques can be productively applied, but limitations in accuracy and robustness must be overcome before that promise can be fully realized. Combining retrieval results from systems built on various errorful representations of the same collection offers some potential to address these challenges. This paper explores that potential by applying Generalized Additive Models to optimize the combination of ranked retrieval results obtained using transcripts produced automatically for the same spoken content by substantially different recognition systems. Topic-averaged retrieval effectiveness better than any previously reported for the same collection was obtained, and even larger gains are apparent when using an alternative measure emphasizing results on the most difficult topics.