Document Detail


Classification of cries of infants with cleft-palate using parallel hidden Markov models.
MedLine Citation:
PMID:  18368431     Owner:  NLM     Status:  MEDLINE    
Abstract/OtherAbstract:
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.
Authors:
Dror Lederman; Ehud Zmora; Stephanie Hauschildt; Angelika Stellzig-Eisenhauer; Kathleen Wermke
Publication Detail:
Type:  Evaluation Studies; Journal Article     Date:  2008-03-27
Journal Detail:
Title:  Medical & biological engineering & computing     Volume:  46     ISSN:  1741-0444     ISO Abbreviation:  Med Biol Eng Comput     Publication Date:  2008 Oct 
Date Detail:
Created Date:  2008-09-23     Completed Date:  2009-07-06     Revised Date:  -    
Medline Journal Info:
Nlm Unique ID:  7704869     Medline TA:  Med Biol Eng Comput     Country:  United States    
Other Details:
Languages:  eng     Pagination:  965-75     Citation Subset:  IM    
Affiliation:
Department of ECE, Ben-Gurion University of the Negev, Beer-Sheva, Israel. drorle@ee.bgu.ac.il
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MeSH Terms
Descriptor/Qualifier:
Aging / physiology
Algorithms
Cleft Palate / physiopathology*
Crying / physiology*
Humans
Infant
Markov Chains
Pattern Recognition, Automated / methods
Signal Processing, Computer-Assisted
Sound Spectrography

From MEDLINE®/PubMed®, a database of the U.S. National Library of Medicine


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