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Child behavior in the natural environment is a subject that is relevant for many areas of social science and bio-behavioral research. However, its measurement is currently based mainly on subjective approaches such as parent questionnaires or clinical observation. This study demonstrates an objective and unobtrusive child vocal behavior measurement and monitoring approach using daylong audio recordings of children in the natural home environment. Our previous research has shown significant performance in childhood autism identification. However, there remains the question of why it works. In the previous study, the focus was more on the overall performance and data-driven modeling without regard to the meaning of underlying features. Even if a high risk of autism is predicted, specific information about child behavior that could contribute to the automated categorization was not further explored. This study attempts to clarify this issue by exploring the details of underlying features and uncovering additional behavioral information buried within the audio streams. It was found that much child vocal behavior can be measured automatically by applying signal processing and pattern recognition technologies to daylong audio recordings. By combining many such features, the model achieves an overall autism identification accuracy of 94% (N=226). Similar to many emerging non-invasive and telemonitoring technologies in health care, this approach is believed to have great potential in child development research, clinical practice and parenting.