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

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...

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Publicado en:Medical & Biological Engineering & Computing Vol. 46; no. 10; pp. 965 - 976
Autores principales: Lederman D, Zmora E, Hauschildt S, Stellzig-Eisenhauer A, Wermke K, Lederman, Dror, Zmora, Ehud, Hauschildt, Stephanie, Stellzig-Eisenhauer, Angelika, Wermke, Kathleen
Formato: research Journal Article
Publicado: Springer Nature Oct2008
Acceso en línea:Ver este registro en EBSCOhost
Sumario: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.