Cry based infant pathology classification using GMMs
Highlights We characterize the distributions of the acoustic features of infant cry signals with GMMs as a universal background model. An adapted BML method is presented to derive either healthy or pathology subclass models from the GMM UBM. A score level fusion of obtained log likelihood ratio scores from both expiratory and inspiratory sounds is performed. The proposed cry based diagnostic system is used to classify healthy and sick infants. Subjective results indicated that the proposed method can perform better than the Bayesian adaptation.
Speech Commun.