Infant Vocalization

Research on infant vocalizations aims to understand the development of communication and language skills from birth through infancy, leveraging both human annotation and automated analysis techniques. Current studies employ machine learning models, including various neural networks (e.g., convolutional neural networks, ResNets, and transformers) and unsupervised methods like UMAP, to classify vocalizations (e.g., cries, fusses, babbles), analyze acoustic features, and visualize complex vocal interaction patterns within families. These advancements enable more efficient and nuanced analysis of large datasets, potentially leading to improved early detection of developmental disorders and a deeper understanding of the factors influencing early language acquisition.