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

Dror Lederman, Ehud Zmora, Stephanie Hauschildt, Angelika Stellzig-Eisenhauer, Kathleen Wermke

Research output: Contribution to journalArticlepeer-review

30 Scopus citations

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.

Original languageEnglish
Pages (from-to)965-975
Number of pages11
JournalMedical and Biological Engineering and Computing
Volume46
Issue number10
DOIs
StatePublished - 27 Mar 2008

Keywords

  • Classification
  • Cleftlip and palate
  • Infantcry
  • Maximum likelihood
  • Parallel hidden Markov model

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