This paper presents four novel techniques for open-vocabulary spoken document retrieval: a method to detect slots that possibly contain a query feature; a method to estimate occurrence probabilities; a technique that we call collection-wide probability re-estimation and a w eighting scheme which takes advantage of the fact that long query features are detected more reliably. These four techniques have been evaluated using the TREC-6 spoken document retrieval test collection to determine the improvements in retrieval e ectiveness with respect to a baseline retrieval method. Results show that the retrieval e ectiveness can be improved considerably despite the large number of speech recognition errors.
We present an information retrieval system that simultaneously allows to search for text and speech documents. The retrieval system accepts vague queries and performs a best-match search to find those documents that are relevant to the query. The output of the retrieval system is a list of ranked documents where the documents on the top of the list satisfy best the user's information need. The relevance of the documents is estimated by means of metadata (document description vectors). The metadata is automatically generated and it is organized such that queries can be processed efficiently. We introduce a controlled indexing vocabulary for both speech and text documents. The size of the new indexing vocabulary is small (1000 features) compared with the sizes of indexing vocabularies of conventional text retrieval (10000 -100000 features). We show that the retrieval effectiveness based on such a small indexing vocabulary is similar to the retrieval effectiveness of a Boolean retrieval system.
We present a system that retrieves audio recordings containing spoken text in response to a given textual query. In particular, we describe indexing methods that automatically describe the content of the recordings. The indexing methods, which are based on phoneme recognition output, take account of speech recognition errors. Additionally, the indexing methods we present are suitable for a language where many word inflections and compounds may occur. To compare different indexing methods, we have evaluated the retrieval effectiveness on a test collection of 1289 documents and 26 queries. The results show that better effectiveness can be achieved when taking into account the characteristics of the underlying speech recognition system.
We present here the realisation of a cross-language speech retrieval system which retrieves German speech documents in response to user queries specified as Prench text. This has been achieved through the integration of two existing modules of the SPIDER information retrieval system, namely the query pseudo-translation module and the speech retrieval module. Our approach to cross-language retrieval uses an automatically constructed corpus-based information structure called a simihwity thesaurus. A similarity thesaurus can be constructed over any loosely comparable corpus -a parallel corpus is not necessary. The similarity thesaurus used here was constructed over a 330 MByte corpus of comparable German and Fkench news stones. Our speech retrieval module is based on a speaker-independent phoneme recognize and it indexes speech documents by N-grams of phonemic features. The speech retrieval module includes an additional probabilistic matching technique designed to aid retrieval from erroneous data such as the phonemic output of the speech recognition process. We have evaluated our cross-language speech retrieval system over a collection of 30 hours (3.4 GBytes) of German speech, comparing the effectiveness of l%nch queries (cross-language) against performance on equivalent German queries (mon~lingual). It must be stressed that this work represents our first step in the direction of cross-language speech retrieval. Our aim here is to establish a bmefine of performance on this task, against which we can then measure the success of our continuing research in this area.
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