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Summary: Objectives. Scientific and clinical advances in perinatology and neonatology have enhanced the chances of survival of preterm and very low weight neonates. Infant cry analysis is a suitable noninvasive complementary tool to assess the neurologic state of infants particularly important in the case of preterm neonates. This article aims at exploiting differences between full-term and preterm infant cry with robust automatic acoustical analysis and data mining techniques. Study design. Twenty-two acoustical parameters are estimated in more than 3000 cry units from cry recordings of 28 full-term and 10 preterm newborns. Methods. Feature extraction is performed through the BioVoice dedicated software tool, developed at the Biomedical Engineering Lab, University of Firenze, Italy. Classification and pattern recognition is based on genetic algorithms for the selection of the best attributes. Training is performed comparing four classifiers: Logistic Curve, Multilayer Perceptron, Support Vector Machine, and Random Forest and three different testing options: full training set, 10-fold crossvalidation, and 66% split. Results. Results show that the best feature set is made up by 10 parameters capable to assess differences between preterm and full-term newborns with about 87% of accuracy. Best results are obtained with the Random Forest method (receiver operating characteristic area, 0.94). Conclusions. These 10 cry features might convey important additional information to assist the clinical specialist in the diagnosis and follow-up of possible delays or disorders in the neurologic development due to premature birth in this extremely vulnerable population of patients. The proposed approach is a first step toward an automatic infant cry recognition system for fast and proper identification of risk in preterm babies.

Smartphone technology provides new opportunities for recording standardized voice samples of patients and sending the files by e-mail to the voice laboratory. This drastically improves the collection of baseline data, as used in research on efficiency of voice treatments. However, the basic requirement is the suitability of smartphones for recording and digitizing pathologic voices (mainly characterized by period perturbations and noise) without significant distortion. In this experiment, two smartphones (a very inexpensive one and a high-level one) were tested and compared with direct microphone recordings in a soundproof room. The voice stimuli consisted in synthesized deviant voice samples (median of fundamental frequency: 120 and 200 Hz) with three levels of jitter and three levels of added noise. All voice samples were analyzed using PRAAT software. The results show high correlations between jitter, shimmer, and noise-to-harmonics ratio measured on the recordings via both smartphones, the microphone, and measured directly on the sound files from the synthesizer. Smartphones thus appear adequate for reliable recording and digitizing of pathologic voices.

Rapid maxillary expansion causes a slight phonetic change in the acoustical parameters of both consonants and vowels. The two-arm Hyrax caused less speech impairment than the four-arm Hyrax during the treatment.