期刊名称：International Journal of Advanced Robotic Systems
印刷版ISSN：1729-8806
电子版ISSN：1729-8814
出版年度：2012
卷号：9
期号：5
页码：164
DOI：10.5772/52454
语种：English
出版社：SAGE Publications
摘要：This paper addresses the problem of spoken document retrieval under noisy conditions by incorporating sound selection of a basic unit and an output form of a speech recognition system. Syllable fragment is combined with a confusion network in a spoken document retrieval task. After selecting an appropriate syllable fragment, a lattice is converted into a confusion network that is able to minimize the word error rate instead of maximizing the whole sentence recognition rate. A vector space model is adopted in the retrieval task where tf-idf weights are derived from the posterior probability. The confusion network with syllable fragments is able to improve the mean of average precision (MAP) score by 0.342 and 0.066 over one-best scheme and the lattice.
关键词：spoken document retrieval; lattice; confusion network; syllable fragment
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