Using automatic speech recognition to assess the reading proficiency of a diverse sample of middle school students

This paper describes a study exploring automated assessment of reading proficiency, in terms of oral reading and reading comprehension, for a middle school population including students with reading disabilities and low reading proficiency, utilizing automatic speech recognition technology. We build statistical models using features related to fluency, pronunciation, and reading accuracy to predict three dependent variables: two are related to accuracy and speed of reading, the third is a reading comprehension measure from a state assessment of reading. The correlation coefficients of the best-performing linear regression models range from r = 0.64 (reading comprehension score) to 0.98 (correctly read words per minute). We further look at the features with the highest absolute regression weights in the three models and find that most of them fall into the classes of reading accuracy and reading speed. Still, features from the pronunciation class and other fluency features, e.g., relating to silences in the read speech, are also represented in the regression models but with less emphasis.