  From crying to babbling and then to speech, infant's vocal tract goes through
anatomic restructuring. In this paper, we propose a non-invasive fast method of
using infant cry signals with convolutional neural network (CNN) based age
classification to diagnose the abnormality of the vocal tract development as
early as 4-month age. We study F0, F1, F2, and spectrograms and relate them to
the postnatal development of infant vocalization. A novel CNN based age
classification is performed with binary age pairs to discover the pattern and
tendency of the vocal tract changes. The effectiveness of this approach is
evaluated on Baby2020 with healthy infant cries and Baby Chillanto database
with pathological infant cries. The results show that our approach yields
79.20% accuracy for healthy cries, 84.80% for asphyxiated cries, and 91.20% for
deaf cries. Our method first reveals that infants' vocal tract develops to a
certain level at 4-month age and infants can start controlling the vocal folds
to produce discontinuous cry sounds leading to babbling. Early diagnosis of
growth abnormality of the vocal tract can help parents keep vigilant and adopt
medical treatment or training therapy for their infants as early as possible.
