Oral Reading

Oral reading assessment is crucial for evaluating literacy skills, but traditional methods are time-consuming and resource-intensive. Current research focuses on automating this process using machine learning, particularly deep learning models like wav2vec 2.0 and Whisper, to analyze audio recordings of oral reading, assessing fluency, accuracy, and even predicting lexical skills based on acoustic features like pause duration and syllable rate. These automated systems offer the potential for efficient, large-scale literacy screening and improved early intervention for reading difficulties, particularly in resource-constrained settings.