Thematic indexing of spoken documents by using self-organizing maps
Speech Communication
Ferret: a toolkit for content-based similarity search of feature-rich data
Proceedings of the 1st ACM SIGOPS/EuroSys European Conference on Computer Systems 2006
Spoken Content Retrieval: A Survey of Techniques and Technologies
Foundations and Trends in Information Retrieval
Information-theoretic term weighting schemes for document clustering
Proceedings of the 13th ACM/IEEE-CS joint conference on Digital libraries
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There has been a considerable focus on information retrieval for multimedia databases. When speech is used as the source material for multimedia indexing, the effect of transcriber error on retrieval effectiveness must be considered. This paper describes a method for measuring the relevance of documents to queries when information about the probability of word transcription error is available. To support the use of this technique, a method is presented for estimating word error probability in speech recognition engines that use word graphs (lattices). An information retrieval experiment using this technique on a large corpus of spoken documents is discussed. The method was able to reduce the difference in retrieval effectiveness between reference texts and hypothesized texts by 13-38 % depending on the size of the document set.