Pitt at CLEF05: Data Fusion for Spoken Document Retrieval

Abstract. This paper describes an investigation of data fusion techniques for spoken document retrieval. The effectiveness of retrievals solely based on the outputs from automatic speech recognition (ASR) is subject to the recognition errors introduced by the ASR process. This is especially true for retrievals on Malach test collection, whose ASR outputs have average word error rate (WER) of 35explored data fusion techniques for integrating the manually generated metadata information, which is provided for every Malach document, with the ASR outputs. We concentrated our effort on the post-search data fusion techniques, where multiple retrieval results using automatic generated outputs or human metadata were combined. Our initial studies indicated that a simple unweighted combination method (i.e., CombMNZ) that had demonstrated to be useful in written text retrieval environment only generated significant 38in retrieval effectiveness (measured by Mean Average Precision) for our task ...

• Type: Conference
• Year: 2005
• Where: CLEF
• Authors: Daqing He, Jae-wook Ahn
• Keywords: ASR Outputs, CLEF 2005, Data Fusion, Data Fusion Techniques