Classification of cries of infants with cleft-palate using parallel hidden Markov models.
This paper addresses the problem of classification of infants with cleft palate. A hidden Markov model (HMM)-based cry classification algorithm is presented. A parallel HMM (PHMM) for coping with age masking, based on a maximum-likelihood decision rule, is introduced. The performance of the proposed...
| Publicado en: | Medical & Biological Engineering & Computing Vol. 46; no. 10; pp. 965 - 976 |
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| Autores principales: | , , , , , , , , , |
| Formato: | research Journal Article |
| Publicado: |
Springer Nature
Oct2008
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=105556726&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 105556726 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 01400118 PO0 jtl: Medical & Biological Engineering & Computing issn: 01400118 maglogo: N pubinfo: dt: Oct2008 vid: 46 iid: 10 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 105556726 NLM18368431 2010048985 10.1007/s11517-008-0334-y NLM18368431 105556726 ppf: 965 ppct: 11 formats: fmt: @attributes: type: P tig: atl: Classification of cries of infants with cleft-palate using parallel hidden Markov models. aug: au: Lederman D Zmora E Hauschildt S Stellzig-Eisenhauer A Wermke K Lederman, Dror Zmora, Ehud Hauschildt, Stephanie Stellzig-Eisenhauer, Angelika Wermke, Kathleen affil: Department of ECE, Ben-Gurion University of the Negev, Beer-Sheva, Israel sug: subj: Cleft Palate Physiopathology Crying Aging Physiology Algorithms Infant Information Science Methods Probability Signal Processing, Computer Assisted Sound Spectrography Human Infant: 1-23 months ab: This paper addresses the problem of classification of infants with cleft palate. A hidden Markov model (HMM)-based cry classification algorithm is presented. A parallel HMM (PHMM) for coping with age masking, based on a maximum-likelihood decision rule, is introduced. The performance of the proposed algorithm under different model parameters and different feature sets is studied using a database of cries of infants with cleft palate (CLP). The proposed algorithm yields an average of 91% correct classification rate in a subject- and age-dependent experiment. In addition, it is shown that the PHMM significantly outperforms the HMM performance in classification of cries of CLP infants of different ages. pubtype: Academic Journal doctype: research Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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