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 language | English |
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Pages (from-to) | 965-975 |
Number of pages | 11 |
Journal | Medical and Biological Engineering and Computing |
Volume | 46 |
Issue number | 10 |
DOIs | |
State | Published - 27 Mar 2008 |
Keywords
- Classification
- Cleftlip and palate
- Infantcry
- Maximum likelihood
- Parallel hidden Markov model
ASJC Scopus subject areas
- Biomedical Engineering
- Computer Science Applications