  Child-directed speech (CDS) is a particular type of speech that adults use
when addressing young children. Its properties also change as a function of
extralinguistic factors, such as age of the child being addressed. Access to
large amounts of representative and varied CDS would be useful for child
language research, as this would enable controlled computational modeling
experiments of infant language acquisition with realistic input in terms of
quality and quantity. In this study, we describe an approach to model
age-dependent linguistic properties of CDS using a language model (LM) trained
on CDS transcripts and ages of the recipient children, as obtained from North
American English corpora of the CHILDES database. The created LM can then be
used to stochastically generate synthetic CDS transcripts in an age-appropriate
manner, thereby scaling beyond the original datasets in size. We compare
characteristics of the generated CDS against the real speech addressed at
children of different ages, showing that the LM manages to capture
age-dependent changes in CDS, except for a slight difference in the effective
vocabulary size. As a side product, we also provide a systematic
characterization of age-dependent linguistic properties of CDS in CHILDES,
illustrating how all measured aspects of the CDS change with children's age.
