We make use of information inside infant’s cry signal in order to identify the infant’s psychological condition. Gaussian mixture models (GMMs) are applied to distinguish between healthy full-term and premature infants, and those with specific medical problems available in our cry database. Cry pattern for each pathological condition is created by using adapted boosting mixture learning (BML) method to estimate mixture model parameters. In the first experiment, test results demonstrate that the introduced adapted BML method for learning of GMMs has a better performance than conventional EM-based reestimation algorithm as a reference system in multipathological classification task. This newborn cry-based diagnostic system (NCDS) extracted Mel-frequency cepstral coefficients (MFCCs) as a feature vector for cry patterns of newborn infants. In binary classification experiment, the system discriminated a test infant’s cry signal into one of two groups, namely, healthy and pathological based on MFCCs. The binary classifier achieved a true positive rate of 80.77% and a true negative rate of 86.96% which show the ability of the system to correctly identify healthy and diseased infants, respectively.

HighlightsWe 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.

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