An ASR-Based Tutor for Learning to Read: How to Optimize Feedback to
  First Graders
Yu Bai Cristian Tejedor-Garcia Ferdy Hubers Catia Cucchiarini Helmer Strik
The interest in employing automatic speech recognition (ASR) in applications
for reading practice has been growing in recent years. In a previous study, we
presented an ASR-based Dutch reading tutor application that was developed to
provide instantaneous feedback to first-graders learning to read. We saw that
ASR has potential at this stage of the reading process, as the results
suggested that pupils made progress in reading accuracy and fluency by using
the software. In the current study, we used children's speech from an existing
corpus (JASMIN) to develop two new ASR systems, and compared the results to
those of the previous study. We analyze correct/incorrect classification of the
ASR systems using human transcripts at word level, by means of evaluation
measures such as Cohen's Kappa, Matthews Correlation Coefficient (MCC),
precision, recall and F-measures. We observe improvements for the newly
developed ASR systems regarding the agreement with human-based judgment and
correct rejection (CR). The accuracy of the ASR systems varies for different
reading tasks and word types. Our results suggest that, in the current
configuration, it is difficult to classify isolated words. We discuss these
results, possible ways to improve our systems and avenues for future research.