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dc.contributor.authorFrankel, Joe
dc.contributor.authorKing, Simon
dc.date.accessioned2006-05-19T15:45:37Z
dc.date.available2006-05-19T15:45:37Z
dc.date.issued2001
dc.identifier.citationFrankel, Joe / King, Simon (2001): "ASR - articulatory speech recognition", In EUROSPEECH-2001, 599-602.en
dc.identifier.urihttp://www.isca-speech.org/archive/eurospeech_2001/index.html
dc.identifier.urihttp://hdl.handle.net/1842/1140
dc.description.abstractWe propose that using a continuous trajectory model to describe an articulatory-based feature set will address some of the shortcomings inherent in the hidden Markov model (HMM) as a model for speech recognition. The articulatory parameters allow us to explicitly model effects such as co-articulation and assimilation. A linear dynamic model (LDM) is used to capture the characteristics of each segment type. These models are well suited to describing smoothly varying, continuous, yet noisy trajectories, such as we find present in speech data. Experimentation has been based on data for a single speaker from the MOCHA corpus. This consists of parallel acoustic and recorded articulatory parameters for 460 TIMIT sentences. We report the results of classification and recognition tasks using both real and recovered articulatory parameters, on their own and in conjunction with acoustic features.en
dc.format.extent63047 bytes
dc.format.extent52793 bytes
dc.format.mimetypeapplication/postscript
dc.format.mimetypeapplication/pdf
dc.language.isoen
dc.publisherInternational Speech Communication Associationen
dc.subjectArticulatory Speech Recognitionen
dc.subjecthidden Markov modelen
dc.titleASR - Articulatory Speech Recognitionen
dc.typeConference Paperen


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