Linked Life Data

18368431

Source:http://linkedlifedata.com/resource/pubmed/id/18368431

Statements in which the resource exists as a subject.
PredicateObject
rdf:type
lifeskim:mentions
pubmed:issue
10
pubmed:dateCreated
2008-9-23
pubmed:abstractText
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.
pubmed:language
eng
pubmed:journal
pubmed:citationSubset
IM
pubmed:status
MEDLINE
pubmed:month
Oct
pubmed:issn
1741-0444
pubmed:author
pubmed:issnType
Electronic
pubmed:volume
46
pubmed:owner
NLM
pubmed:authorsComplete
Y
pubmed:pagination
965-75
pubmed:meshHeading
pubmed:year
2008
pubmed:articleTitle
Classification of cries of infants with cleft-palate using parallel hidden Markov models.
pubmed:affiliation
Department of ECE, Ben-Gurion University of the Negev, Beer-Sheva, Israel. drorle@ee.bgu.ac.il
pubmed:publicationType
Journal Article, Evaluation Studies