Child vocal development is a subject that touches many areas. Its measurement is based mainly on subjective approaches. This study demonstrates an objective and unobtrusive measurement and monitoring approach using daylong audio recordings of the natural environment. Our previous study had shown significant result in automatic child vocalization analysis and childhood autism identification. However, there remains the question of why it works. The previous focus was on the overall performance and data-driven modeling without regard to the meaning of underlying features. Even with a good performance, the information about child vocal behavior that contributes to the result was not explored. This study attempts to explore the underlying features and uncover additional information about child vocal development buried within the audio streams. It was found that child vocal behavior can be measured automatically by applying signal processing and pattern recognition technologies to daylong recordings. By combining such features, a correlation of 0.84 between the estimated vocalization age and the chronological age for children of typical development and 94% accuracy for autism identification are achieved. 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.