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dc.contributor.authorIliya, Sundayen
dc.contributor.authorNeri, Ferranteen
dc.contributor.authorMenzies, Dylanen
dc.contributor.authorCornelius, Pipen
dc.contributor.authorPicinali, Lorenzoen
dc.date.accessioned2016-02-18T10:14:23Z
dc.date.available2016-02-18T10:14:23Z
dc.date.issued2014
dc.identifier.citationIliya, S. et al. (2014) Robust Impaired Speech Segmentation Using Neural Network Mixture Model. 2014 IEEE International Symposium on Signal Processing and Information Technology (ISSPIT), pp. 000444-000449en
dc.identifier.isbn9781479918119
dc.identifier.urihttp://hdl.handle.net/2086/11531
dc.description.abstractThis paper presents a signal processing technique for segmenting short speech utterances into unvoiced and voiced sections and identifying points where the spectrum becomes steady. The segmentation process is part of a system for deriving musculoskeletal articulation data from disordered utterances, in order to provide training feedback for people with speech articulation problem. The approach implement a novel and innovative segmentation scheme using artificial neural network mixture model (ANNMM) for identification and capturing of the various sections of the disordered (impaired) speech signals. This paper also identify some salient features that distinguish normal speech from impaired speech of the same utterances. This research aim at developing artificial speech therapist capable of providing reliable text and audiovisual feed back progress report to the patient.en
dc.language.isoenen
dc.publisherIEEEen
dc.titleRobust Impaired Speech Segmentation Using Neural Network Mixture Modelen
dc.typeConferenceen
dc.identifier.doihttp://dx.doi.org/10.1109/ISSPIT.2014.7300630
dc.researchgroupCentre for Computational Intelligenceen
dc.peerreviewedYesen
dc.funderPetroleum Technology Develop Fund Scholaship, Nigeriaen
dc.funderDMUen
dc.projectidResearchen
dc.researchinstituteInstitute of Artificial Intelligence (IAI)en


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