  Language development researchers are interested in comparing the process of
language learning across languages. Unfortunately, it has been difficult to
construct a consistent quantitative framework for such comparisons. However,
recent advances in AI (Artificial Intelligence) and ML (Machine Learning) are
providing new methods for ASR (automatic speech recognition) and NLP (natural
language processing) that can be brought to bear on this problem. Using the
Batchalign2 program (Liu et al., 2023), we have been transcribing and linking
data for the CHILDES database and have applied the UD (Universal Dependencies)
framework to provide a consistent and comparable morphosyntactic analysis for
27 languages. These new resources open possibilities for deeper crosslinguistic
study of language learning.
