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Information Fusion For Spoken Document Retrieval (2000)

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by Kenney Ng
Venue:in Proc. ICASSP
Citations:20 - 0 self
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BibTeX

@INPROCEEDINGS{Ng00informationfusion,
    author = {Kenney Ng},
    title = {Information Fusion For Spoken Document Retrieval},
    booktitle = {in Proc. ICASSP},
    year = {2000}
}

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Abstract

In this paper we investigate the fusion of different information sources with the goal of improving performance on spoken document retrieval (SDR) tasks. In particular, we explore the use of multiple transcriptions from different automatic speech recognizers, the combination of different types of subword unit indexing terms, and the combination of word and subword-based units. To perform retrieval, we use a novel probabilistic information retrieval model which retrieves documents based on maximum likelihood ratio scores. Experiments on the 1998 TREC-7 SDR task show that the use of these different information fusion approaches can result in significantly improved retrieval performance. 1. INTRODUCTION Spoken document retrieval (SDR) is the task of searching a static collection of recorded speech messages in response to a userspecified natural language text query and returning an ordered list of messages ranked according to their relevance to the query. The development of automatic met...

Keyphrases

spoken document retrieval    information fusion    novel probabilistic information retrieval model    different automatic speech recognizers    different information source    automatic met    static collection    different information fusion approach    different type    subword unit indexing term    maximum likelihood ratio score    subword-based unit    recorded speech message    retrieval performance    introduction spoken document retrieval    trec-7 sdr task show    userspecified natural language text query    ordered list    multiple transcription   

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