Using State-of-the-Art Speech Models to Evaluate Oral Reading Fluency in
  Ghana
Owen Henkel Hannah Horne-Robinson Libby Hills Bill Roberts Joshua McGrane
This paper reports on a set of three recent experiments utilizing large-scale
speech models to evaluate the oral reading fluency (ORF) of students in Ghana.
While ORF is a well-established measure of foundational literacy, assessing it
typically requires one-on-one sessions between a student and a trained
evaluator, a process that is time-consuming and costly. Automating the
evaluation of ORF could support better literacy instruction, particularly in
education contexts where formative assessment is uncommon due to large class
sizes and limited resources. To our knowledge, this research is among the first
to examine the use of the most recent versions of large-scale speech models
(Whisper V2 wav2vec2.0) for ORF assessment in the Global South.
  We find that Whisper V2 produces transcriptions of Ghanaian students reading
aloud with a Word Error Rate of 13.5. This is close to the model's average WER
on adult speech (12.8) and would have been considered state-of-the-art for
children's speech transcription only a few years ago. We also find that when
these transcriptions are used to produce fully automated ORF scores, they
closely align with scores generated by expert human graders, with a correlation
coefficient of 0.96. Importantly, these results were achieved on a
representative dataset (i.e., students with regional accents, recordings taken
in actual classrooms), using a free and publicly available speech model out of
the box (i.e., no fine-tuning). This suggests that using large-scale speech
models to assess ORF may be feasible to implement and scale in lower-resource,
linguistically diverse educational contexts.