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

    32 Scopus citations


    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
    Issue number10
    StatePublished - 27 Mar 2008


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

    ASJC Scopus subject areas

    • Biomedical Engineering
    • Computer Science Applications


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