Lightweight child-worn recorders that collect audio across an entire day allow for a big-data approach to the study of language development. By collecting the child's production and linguistic environment, these recordings offer us a uniquely naturalistic view of everyday language uses. However, such recordings quickly accumulate thousands of hours of audio and require the use of automatic speech processing algorithms. Besides providing ecologically-valid measures of what children hear and say, these recordings can fuel computational models of early language acquisition with what infants truly hear. This opens up new opportunities for running realistic language learning simulations.A first aspect of my doctoral work is dedicated to developing automatic speech processing algorithms for child-centered long-form recordings. In this manuscript, I first show that current state-of-the-art automatic speech recognition systems fail to capture the complexity of naturalistic speech as found in long-forms. I then present our attempt to propose a free, open-source, and more accurate alternative to the LENA proprietary software, which is currently the standard tool for obtaining automatic analyses of long-forms. Using supervised learning methods, my collaborators and I built a suite of speech processing tools to detect voice activity, identify voice signal sources (child vocalizations, female or male speech), count the number of linguistic units (phonemes, syllables, or words), and estima