Speech Recognition Using ARMA Model and Levenberg-Marquardt Algorithm

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dc.contributor.authorJafari, Rezaen
dc.contributor.authorJafari, Amir H.en
dc.date.accessioned2025-02-05T13:21:14Zen
dc.date.available2025-02-05T13:21:14Zen
dc.date.issued2024-08-01en
dc.description.abstractAutoregressive Moving Average (ARMA) is a simple linear model with memory that can be used for speech recognition problems. This is why, this paper utilized the derivation of ARMA model for the speech recognition. The flexibility of ARMA model helps in derivation of an accurate model that recognizes the pronunciation of letter B. The Generalized Partial Autocorrelation (GPAC) analysis has been used for the preliminary identification and the Maximum Likelihood Estimator (Levenberg-Marquardt) is used for the parameter estimations. Several models have been developed to recognize the letter B that are pronounced by a lady 30 times. The simplest model has been chosen at the end. The accuracy of the final model has been checked using χ2 test.en
dc.description.versionAccepted versionen
dc.format.extentPages 351-367en
dc.format.extent17 page(s)en
dc.format.mimetypeapplication/pdfen
dc.identifier.doihttps://doi.org/10.1007/978-3-031-66336-9_25en
dc.identifier.eissn2367-3389en
dc.identifier.isbn978-3-031-66335-2en
dc.identifier.issn2367-3370en
dc.identifier.orcidJafari, Reza [0000-0002-4520-9305]en
dc.identifier.urihttps://hdl.handle.net/10919/124499en
dc.identifier.volume1068en
dc.language.isoenen
dc.publisherSpringeren
dc.rightsIn Copyrighten
dc.rights.urihttp://rightsstatements.org/vocab/InC/1.0/en
dc.subjectSpeech recognitionen
dc.subjectNonlinear optimizationen
dc.subjectResidual analysisen
dc.titleSpeech Recognition Using ARMA Model and Levenberg-Marquardt Algorithmen
dc.title.serialIntelligent Systems and Applicationsen
dc.typeConference proceedingen
dc.type.dcmitypeTexten
dc.type.otherProceedings Paperen
dc.type.otherBook in seriesen
pubs.finish-date2024-09-06en
pubs.organisational-groupVirginia Techen
pubs.organisational-groupVirginia Tech/Innovation Campusen
pubs.start-date2024-09-05en

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