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From crying to babbling and then to speech, infant's vocal tract goes throughanatomic restructuring. In this paper, we propose a non-invasive fast method ofusing infant cry signals with convolutional neural network (CNN) based ageclassification to diagnose the abnormality of the vocal tract development asearly as 4-month age. We study F0, F1, F2, and spectrograms and relate them tothe postnatal development of infant vocalization. A novel CNN based ageclassification is performed with binary age pairs to discover the pattern andtendency of the vocal tract changes. The effectiveness of this approach isevaluated on Baby2020 with healthy infant cries and Baby Chillanto databasewith pathological infant cries. The results show that our approach yields79.20% accuracy for healthy cries, 84.80% for asphyxiated cries, and 91.20% fordeaf cries. Our method first reveals that infants' vocal tract develops to acertain level at 4-month age and infants can start controlling the vocal foldsto produce discontinuous cry sounds leading to babbling. Early diagnosis ofgrowth abnormality of the vocal tract can help parents keep vigilant and adoptmedical treatment or training therapy for their infants as early as possible.

Infant Vocal Tract Development Analysis and Diagnosis by Cry Signals with CNN Age Classification