Automatic assessment of oral reading fluency for Spanish speaking ELs

This article presents an approach to the automatic assessment of the oral reading fluency (ORF) of children in Spain who are learning to read English. We compared different acoustic modeling configurations and adaptation methods to determine the most accurate means of estimating reliable children’s oral reading fluency scores using the standard metric of words correct per minute (WCPM). We addressed the problem of identifying word errors by extracting a series of features in order to learn how the human experts are actually annotating individual words. Experimental results show that the difference between WCPM scores produced by the proposed system and two human judges on the same text is smaller than the average difference between the scores produced by the two judges. In addition, the system scored individual words in texts as correctly or incorrectly read with an accuracy similar to that of human annotators.