Parkinson's Condition Estimation Using Speech Acoustic and Inversely Mapped Articulatory Data

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dc.contributor.authorHahm, Seongjunen_US
dc.contributor.authorWang, Junen_US
dc.contributor.otherNoth E.en_US
dc.contributor.otherSteidl S.en_US
dc.contributor.otherMoller S.en_US
dc.contributor.otherNey H.en_US
dc.contributor.otherMobius B.en_US
dc.contributor.utdAuthorHahm, Seongjun
dc.contributor.utdAuthorWang, Jun
dc.date.accessioned2016-09-27T20:51:05Z
dc.date.available2016-09-27T20:51:05Z
dc.date.created2015-09en_US
dc.description.abstractParkinson's disease is a neurological disorder that affects patient's motor function including speech articulation. There is no cure for Parkinson's disease. Speech and motor function declines as the disease progresses. Automatic assessment of the disease condition may advance the treatment of Parkinson's disease with objective, inexpensive measures. Speech acoustics, which can be easily obtained from patients, has been used for automatic assessment. The use of information in motor function of articulator (e.g., jaw, tongue, or lips) has rarely been investigated. In this paper, we proposed an approach of automatic assessment of Parkinson's condition using both acoustic data and acoustically-inverted articulatory data. The quasi-articulatory features were obtained from the Parkinson's acoustic speech data using acoustic-to-articulatory inverse mapping. Support vector regression (SVR) and deep neural network (DNN) regression were used in the experiment. Results indicated adding articulatory data to acoustic data can improve the performance of using acoustic data only, for both SVR and DNN. In addition, deep neural network outperformed support vector regression on the same data features measured with Pearson correlation but not with Spearman correlation. The implications of our approach with further improvement were discussed.en_US
dc.identifier.bibliographicCitationHahm, S., and J. Wang. 2015. "Parkinson's condition estimation using speech acoustic and inversely mapped articulatory data." INTERSPEECH 2015 (16th Annual conference of the International Speech Communication Association), 513-517.-en_US
dc.identifier.issn2308-457Xen_US
dc.identifier.urihttp://hdl.handle.net/10735.1/5095
dc.identifier.volume2015en_US
dc.language.isoenen_US
dc.publisherInternational Speech and Communication Associationen_US
dc.rights©2015 ISCAen_US
dc.source.journalINTERSPEECHen_US
dc.subjectParkinson's diseaseen_US
dc.subjectNeural networks (Neurobiology)en_US
dc.subjectArticulation disordersen_US
dc.subjectSpeech Acousticsen_US
dc.subjectMotor abilityen_US
dc.titleParkinson's Condition Estimation Using Speech Acoustic and Inversely Mapped Articulatory Dataen_US
dc.type.genrearticleen_US

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