Automatic infant cry analysis and recognition

Abstract

This dissertation is a report of my investigation on introducing modem speech processing/recognition techniques to the field of infant cry research, and on developing efficient and effective methodologies for automatic assessment of the physical/emotional situation of infants using the information derived from the cry signals. I first identify some problems facing present infant cry research, especially those obstructing the practical applications of the results generated from basic research. By demonstrating the similarities between infant cry generation and adult speech generation, I establish the theoretical foundation for the development of my new automatic cry processing/analysis techniques. In particular, I develop the new concept of cry phonemes as an effective method for representing cry signals for automatic cry analysis. Based on the cry phonemes, I further define a composite parameter, the H-value, which can be calculated from the cry signal, and is found to be a reliable indicator of the distress level of the infant. Using these new concepts, I design two automatic infant cry analysis systems. One system is based on my newly developed nonparametric VQ-kernel classifier, and the other system is based on the Hidden Markov Model technique. Each of these systems estimates the N-value from the cry signal automatically. This, in turn, is utilized in the automatic assessment of the infant's distress level. The performance of these two systems was evaluated with cries uttered by 36 infants. I found that both systems give assessments of infants' distress levels consistent with the perceptions of experienced parents who listened to the recording of the same cries. This demonstrates the effectiveness of my newly developed techniques. In addition, the methodologies developed in this research can be easily generalized and applied to other problems of normal and abnormal infant cry analysis.