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

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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.

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Acknowledgments

This research is dedicated to the memory of Prof. Arnon Cohen who initiated the cry classification project. The authors are grateful to Dr. Joseph Tabrikian from Ben-Gurion University of the Negev, for reviewing this manuscript and for his valuable remarks. The authors are also grateful to the anonymous reviewers whose comments helped us to substantially improve the paper.

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Authors and Affiliations

  1. Department of ECE, Ben-Gurion University of the Negev, P.O. Box 653, 84105, Beer-Sheva, Israel

    Dror Lederman

  2. Department of Neonatology, Soroka Medical Center, Ben-Gurion University Health Sciences Center, Beer-Sheva, Israel

    Ehud Zmora

  3. Department of Orthodontics, Julius-Maximilians-University, Wuerzburg, Germany

    Stephanie Hauschildt & Angelika Stellzig-Eisenhauer

  4. Center for Pre-Speech Development and Developmental Disorders, Department of Orthodontics, Julius-Maximilians-University, Wuerzburg, Germany

    Kathleen Wermke

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  1. Dror Lederman
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  2. Ehud Zmora
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  3. Stephanie Hauschildt
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  4. Angelika Stellzig-Eisenhauer
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  5. Kathleen Wermke
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Correspondence to Dror Lederman.

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Lederman, D., Zmora, E., Hauschildt, S. et al. Classification of cries of infants with cleft-palate using parallel hidden Markov models. Med Biol Eng Comput 46, 965–975 (2008). https://doi.org/10.1007/s11517-008-0334-y

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  • DOI: https://doi.org/10.1007/s11517-008-0334-y

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