Using speech data to accelerate progress toward early detection and prevention of reading challenges

Through ReadNet, we are creating an open database with unique potential for improving reading outcomes for U.S. children. This database will include tens of thousands of anonymized, annotated speech samples and direct assessment data of reading skills taken from students who were screened for risk of reading difficulties when they were in kindergarten and include the individual reading outcomes of these same students in later grades.

We will share this powerful dataset in an open format and promote its use through Kaggle Competitions to improve the accuracy of speech recognition technology, particularly with respect to dialectical variation. We expect this will enable better tools for automated in-school screening of all children that are efficient, valid, scalable, and equitable, and also allow us to predict future reading challenges from kindergarten speech data.

Research for the ReadNet Project is being conducted through the Gabrieli Lab, Quantitative Methodology and Innovation at Florida Center for Reading Research, and Senseable Intelligence Group. It is supported by generous gifts from Schmidt Futures and Citadel founder and CEO Ken Griffin.