  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.
