Med Biol Eng Comput. 2008 Oct;46(10):965-75. Epub 2008 Mar 27.

Classification of cries of infants with cleft-palate using parallel hidden Markov models.

Lederman D, Zmora E, Hauschildt S, Stellzig-Eisenhauer A, Wermke K.

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Department of ECE, Ben-Gurion University of the Negev, Beer-Sheva, Israel. drorle@ee.bgu.ac.il

Abstract

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.

PMID:: 18368431; [PubMed - indexed for MEDLINE]

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