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dc.contributor.authorHochberg, Mike
dc.contributor.authorRenals, Steve
dc.contributor.authorRobinson, Tony
dc.contributor.authorCook, Gary
dc.date.accessioned2006-06-22T12:43:31Z
dc.date.available2006-06-22T12:43:31Z
dc.date.issued1995
dc.identifier.citationIn Proc IEEE ICASSP, pages 69-72, Detroit, 1995.en
dc.identifier.urihttp://hdl.handle.net/1842/1275
dc.description.abstractABBOT is the hybrid connectionist-hidden Markov model (HMM) large-vocabulary continuous speech recognition (CSR) system developed at Cambridge University. This system uses a recurrent network to estimate the acoustic observation probabilities within an HMM framework. A major advantage of this approach is that good performance is achieved using context-independent acoustic models and requiring many fewer parameters than comparable HMM systems. This paper presents substantial performance improvements gained from new approaches to connectionist model combination and phone-duration modeling. Additional capability has also been achieved by extending the decoder to handle larger vocabulary tasks (20,000 words and greater) with a trigram language model. This paper describes the recent modifications to the system and experimental results are reported for various test and development sets from the November 1992, 1993, and 1994 ARPA evaluations of spoken language systems.en
dc.format.extent25117 bytes
dc.format.mimetypeapplication/octet-stream
dc.language.isoen
dc.publisherIEEEen
dc.titleRecent improvements to the Abbot large vocabulary CSR system.en
dc.typeConference Paperen


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