  Spontaneous conversations in real-world settings such as those found in
child-centered recordings have been shown to be amongst the most challenging
audio files to process. Nevertheless, building speech processing models
handling such a wide variety of conditions would be particularly useful for
language acquisition studies in which researchers are interested in the
quantity and quality of the speech that children hear and produce, as well as
for early diagnosis and measuring effects of remediation. In this paper, we
present our approach to designing an open-source neural network to classify
audio segments into vocalizations produced by the child wearing the recording
device, vocalizations produced by other children, adult male speech, and adult
female speech. To this end, we gathered diverse child-centered corpora which
sums up to a total of 260 hours of recordings and covers 10 languages. Our
model can be used as input for downstream tasks such as estimating the number
of words produced by adult speakers, or the number of linguistic units produced
by children. Our architecture combines SincNet filters with a stack of
recurrent layers and outperforms by a large margin the state-of-the-art system,
the Language ENvironment Analysis (LENA) that has been used in numerous child
language studies.
