  Self-supervised techniques for learning speech representations have been
shown to develop linguistic competence from exposure to speech without the need
for human labels. In order to fully realize the potential of these approaches
and further our understanding of how infants learn language, simulations must
closely emulate real-life situations by training on developmentally plausible
corpora and benchmarking against appropriate test sets. To this end, we propose
a language-acquisition-friendly benchmark to probe spoken language models at
the lexical and syntactic levels, both of which are compatible with the
vocabulary typical of children's language experiences. This paper introduces
the benchmark and summarizes a range of experiments showing its usefulness. In
addition, we highlight two exciting challenges that need to be addressed for
further progress: bridging the gap between text and speech and between clean
speech and in-the-wild speech.
