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Exploring fusion in a spontaneous speech retrieval task

In this paper we present two novel model fusion techniques. We fuse together results from several Information Retrieval models or variations of the models. We test them on a collection of spontaneous speech transcripts. We also fuse results obtained with different documents representations (automatic transcripts or manual data). Our first fusion model is training the weighs based on the training data, but in an efficient and novel way. The second fusion model works for results with high variation, such as results obtained from automatic vs. manual document representations.