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 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.
Lederman
Dror
D
Department of ECE, Ben-Gurion University of the Negev, Beer-Sheva, Israel. drorle@ee.bgu.ac.il
Zmora
Ehud
E
Hauschildt
Stephanie
S
Stellzig-Eisenhauer
Angelika
A
Wermke
Kathleen
K
D000375:Aging / Q000502:physiology; D000465:Algorithms; D002972:Cleft Palate / Q000503:physiopathology*; D003448:Crying / Q000502:physiology*; D006801:Humans; D007223:Infant; D008390:Markov Chains; D010363:Pattern Recognition, Automated / Q000379:methods; D012815:Signal Processing, Computer-Assisted; D013018:Sound Spectrography