Neural learning for articulatory speech synthesis under different statistical characteristics of acoustic input patterns

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dc.authorid0000-0002-8291-1419
dc.authorid0000-0002-2126-8757
dc.contributor.authorAltun, H
dc.contributor.authorCurtis, KM
dc.contributor.authorYalcinoz, T
dc.date.accessioned2019-08-01T13:38:39Z
dc.date.available2019-08-01T13:38:39Z
dc.date.issued2003
dc.departmentNiğde ÖHÜ
dc.description.abstractInput data representation is highly decisive in neural learning in terms of convergence. In this paper, within an analytical and statistical framework, the effect of the distribution characteristics of the input pattern vectors on the performance of the back-propagation (BP) algorithm is established for a function approximation problem, where parameters of an articulatory speech synthesizer are estimated from acoustic input data. The aim is to determine the optimum statistical characteristics of the acoustic input patterns in order to improve neural learning. Improvement is obtained through a modification of the statistical characteristics of the input data, which reduces effectively the occurrence of node saturation in the hidden layer. (C) 2002 Elsevier Science Ltd. All rights reserved.
dc.identifier.doi10.1016/S0045-7906(02)00055-1
dc.identifier.endpage702
dc.identifier.issn0045-7906
dc.identifier.issue6
dc.identifier.scopus2-s2.0-0037708380
dc.identifier.scopusqualityQ1
dc.identifier.startpage687
dc.identifier.urihttps://dx.doi.org/10.1016/S0045-7906(02)00055-1
dc.identifier.urihttps://hdl.handle.net/11480/5703
dc.identifier.volume29
dc.identifier.wosWOS:000183906400003
dc.identifier.wosqualityQ4
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.institutionauthor[0-Belirlenecek]
dc.language.isoen
dc.publisherPERGAMON-ELSEVIER SCIENCE LTD
dc.relation.ispartofCOMPUTERS & ELECTRICAL ENGINEERING
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.subjectback-propagation algorithm
dc.subjectstatistical dependency of neural learning
dc.subjectarticulatory speech synthesizer
dc.titleNeural learning for articulatory speech synthesis under different statistical characteristics of acoustic input patterns
dc.typeArticle

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