  Reading fluency assessment is a critical component of literacy programmes,
serving to guide and monitor early education interventions. Given the resource
intensive nature of the exercise when conducted by teachers, the development of
automatic tools that can operate on audio recordings of oral reading is
attractive as an objective and highly scalable solution. Multiple complex
aspects such as accuracy, rate and expressiveness underlie human judgements of
reading fluency. In this work, we investigate end-to-end modeling on a training
dataset of children's audio recordings of story texts labeled by human experts.
The pre-trained wav2vec2.0 model is adopted due its potential to alleviate the
challenges from the limited amount of labeled data. We report the performance
of a number of system variations on the relevant measures, and also probe the
learned embeddings for lexical and acoustic-prosodic features known to be
important to the perception of reading fluency.
