Automatic Assessment of Oral Reading Accuracy for Reading Diagnostics
This work addresses the need for precise reading fluency assessment tools for non-English languages, such as Dutch, to aid in early detection of reading difficulties, though it is incremental as it builds on existing ASR methods.
The study tackled the problem of automatically assessing oral reading accuracy in Dutch using ASR systems, achieving substantial agreement with human evaluations (MCC = .63) and a correlation of r = .45 between confidence scores and word correctness.
Automatic assessment of reading fluency using automatic speech recognition (ASR) holds great potential for early detection of reading difficulties and subsequent timely intervention. Precise assessment tools are required, especially for languages other than English. In this study, we evaluate six state-of-the-art ASR-based systems for automatically assessing Dutch oral reading accuracy using Kaldi and Whisper. Results show our most successful system reached substantial agreement with human evaluations (MCC = .63). The same system reached the highest correlation between forced decoding confidence scores and word correctness (r = .45). This system's language model (LM) consisted of manual orthographic transcriptions and reading prompts of the test data, which shows that including reading errors in the LM improves assessment performance. We discuss the implications for developing automatic assessment systems and identify possible avenues of future research.