This paper describes a spoken document retrieval system, combining the ABBOT large vocabulary continuous speech recognition (LVCSR) system developed by Cambridge University, Sheffield University and Softsound, and the PRISE information retrieval engine developed by NIST. The system was constructed to enable us to participate in the TREC 6 Spoken Document Retrieval experimental evaluation. Our key aims in this work were to produce a complete system for the SDR task, to investigate the effect of a word error rate of 30-50% on retrieval performance and to investigate the integration of LVCSR and word spotting in a retrieval task.
This paper describes a spoken document retrieval (SDR) system for British and North American Broadcast News. The system is based on a connectionist large vocabulary speech recognizer and a probabilistic information retrieval system. We discuss the development of a realtime Broadcast News speech recognizer, and its integration into an SDR system. Two advances were made for this task: automatic segmentation and statistical query expansion using a secondary corpus. Precision and recall results using the Text Retrieval Conference (TREC) SDR evaluation infrastructure are reported throughout the paper, and we discuss the application of these developments to a large scale SDR task based on an archive of British English broadcast news.
This paper describes the THISL news retrieval system which maintains a n archive of BBC radio and television news recordings. The system uses the ABBOT large vocabulary continuous speech recognition system to transcribe news broadcasts, and the thisllR text retrieval system to index and access the transcripts. Decoding and indexing is performed automatically, and the archive is updated with three hours of new material every day. A web-based interface to the retrieval system has been devised to facilitate access to the archive.
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