A Dataset for measuring reading levels in India at scale
This addresses the challenge of assessing reading skills in India at scale, though it is incremental as it builds on existing ASR methods.
The authors tackled the problem of measuring reading levels in India by creating the ASER dataset of children's speech in Hindi, Marathi, and English, achieving 86% prediction accuracy for English using an ASR-based classifier.
One out of four children in India are leaving grade eight without basic reading skills. Measuring the reading levels in a vast country like India poses significant hurdles. Recent advances in machine learning opens up the possibility of automating this task. However, the datasets of children's speech are not only rare but are primarily in English. To solve this assessment problem and advance deep learning research in regional Indian languages, we present the ASER dataset of children in the age group of 6-14. The dataset consists of 5,301 subjects generating 81,330 labeled audio clips in Hindi, Marathi and English. These labels represent expert opinions on the child's ability to read at a specified level. Using this dataset, we built a simple ASR-based classifier. Early results indicate that we can achieve a prediction accuracy of 86% for the English language. Considering the ASER survey spans half a million subjects, this dataset can grow to those scales.