One of the major challenges for spoken document retrieval is how to handle speech recognition errors within the target documents. Query expansion is promising for this challenge. In this paper, we apply relevance models, a type of query expansion method, for the spoken document passage retrieval task. We adapted the original relevance model for passage retrieval. We also extended it to benefit from massive collections of Web documents for query expansion. Through our experimental evaluation, we found that our relevance model successfully improved the retrieval performance. We also found that using Web documents was effective when the transcription of the target documents had a high word error rate.
Bibliographic reference. Akiba, Tomoyosi / Honda, Koichiro (2011): "Effects of query expansion for spoken document passage retrieval", In INTERSPEECH-2011, 2137-2140.