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dc.contributor.authorKershaw, Dan
dc.contributor.authorRobinson, Tony
dc.contributor.authorRenals, Steve
dc.coverage.spatial4en
dc.date.accessioned2006-05-15T12:07:39Z
dc.date.available2006-05-15T12:07:39Z
dc.date.issued1996-10
dc.identifier.citationSpoken Language, 1996. ICSLP 96. Proceedings., Fourth International Conference on, Volume 3, 3-6 Oct. 1996 Page(s):1325 - 1328.en
dc.identifier.otherDigital Object Identifier 10.1109/ICSLP.1996.607857
dc.identifier.urihttp://ieeexplore.ieee.org/
dc.identifier.urihttp://hdl.handle.net/1842/1067
dc.description.abstractABBOT is a hybrid (connectionist-hidden Markov model) large-vocabulary speech recognition (LVCSR) system, developed at Cambridge University. In this system, a recurrent network maps each acoustic vector to an estimate of the posterior probabilities of the phone classes, which are used as observation probabilities within an HMM. This paper describes the system which participated in the November 1995 ARPA Hub-3 multiple unknown microphones (MUM) evaluation of continuous speech recognition systems, under the guise of the CU-CON system. The emphasis of the paper is on the changes made to the 1994 ABBOT system, specifically to accommodate the H3 task. This includes improved acoustic modelling using limited word-internal context-dependent models, training on the Wall Street Journal secondary channel database, and using the linear input network for speaker and environmental adaptation. Experimental results are reported for various test and development sets from the November 1994 and 1995 ARPA benchmark tests.en
dc.format.extent413140 bytes
dc.format.mimetypeapplication/pdf
dc.language.isoen
dc.publisherIEEEen
dc.titleThe 1995 ABBOT LVCSR system for multiple unknown microphonesen
dc.typeConference Paperen


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