A Dataset for measuring reading levels in India at scale

TL;DR

This paper introduces the ASER dataset, a large-scale collection of children's speech in Indian languages, to automate reading level assessment and support deep learning research in regional languages.

Contribution

It provides a new multilingual dataset of children's speech with reading level labels and demonstrates an initial ASR-based classifier achieving high accuracy in English.

Findings

Achieved 86% accuracy in predicting English reading levels

Created a dataset of 81,330 labeled audio clips in Hindi, Marathi, and English

Enabled scalable assessment of reading skills in India

Abstract

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.