Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/97868
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dc.contributor.authorHuang, Guangpuen
dc.contributor.authorEr, Meng Jooen
dc.date.accessioned2013-07-26T06:36:35Zen
dc.date.accessioned2019-12-06T19:47:31Z-
dc.date.available2013-07-26T06:36:35Zen
dc.date.available2019-12-06T19:47:31Z-
dc.date.copyright2012en
dc.date.issued2012en
dc.identifier.citationHuang, G., & Er, M. J. (2012). Model-based articulatory phonetic features for improved speech recognition. The 2012 International Joint Conference on Neural Networks (IJCNN).en
dc.identifier.urihttps://hdl.handle.net/10356/97868-
dc.description.abstractWe describe a neural based articulatory phonetic inversion model to improve the recognition of the acoustically varying vowels and the syllable initial plosives. The model uses a set of continuous valued articulatory phonetic features (APFs) to explore the interactions between the motor control of articulators and the acoustic phonetic events. We demonstrate that the neural model gives more accurate and robust recognition performance on the TIMIT sentences. The model offers two salient properties: it allows asynchronous feature changes at phoneme boundaries, and it accounts for the dual aspects of human speech production and perception through a heuristic learning algorithm during APFs mapping.en
dc.language.isoenen
dc.rights© 2012 IEEE.en
dc.subjectDRNTU::Engineering::Electrical and electronic engineeringen
dc.titleModel-based articulatory phonetic features for improved speech recognitionen
dc.typeConference Paperen
dc.contributor.schoolSchool of Electrical and Electronic Engineeringen
dc.contributor.conferenceInternational Joint Conference on Neural Networks (2012 : Brisbane, Australia)en
dc.identifier.doi10.1109/IJCNN.2012.6252748en
item.grantfulltextnone-
item.fulltextNo Fulltext-
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