AUTOMATED PROSODY CLASSIFICATION FOR ORAL READING FLUENCY WITH QUADRATIC KAPPA LOSS AND ATTENTIVE X-VECTORS

Automated prosody classification in the context of oral reading fluency is a critical area for the objective evaluation of students? reading proficiency. In this work, we present the largest dataset to date in this domain. It includes spoken phrases from over 1,300 students assessed by multiple trained raters. Moreover, we investigate the usage of X-Vectors and two variations thereof that incorporate weighted attention in classifying prosody correctness. We also evaluate the usage of quadratic weighted kappa loss to better accommodate the inter-rater differences in the dataset. Results indicate improved performance over baseline convolutional and current state-of-the-art models, with prosodic correctness accuracy of 86.4%.