Classification of infant cry vocalizations using artificial neural networks (ANNs)

The analysis of infant cry vocalizations has been the focus of a number of efforts over the past thirty years. Since the infant cry is one of the only means that an infant has for communicating with its care-giving environment, it is thought that information regarding the state of an infant, such as hunger or pain, can be determined from an infant´s cry. To date, research groups have determined that adult listeners can differentiate between different types of cries auditorily, and at least one group has attempted to automate this classification process. This paper presents the results of another attempt at automating the discrimination process, this time using artificial neural networks (ANNs). The input data consists of successive frames of one of two parametric representations generated from the first second of a cry following the application of either an anger, fear, or pain stimulus. From tests conducted to date, it is determined that ANNs are a useful tool for cry classification and merit further study in this domain