UMD Faculty Member Co-Hosts Workshop to Advance Automated Speech Recognition Systems Used for Education

Despite significant advancements in automatic speech recognition (ASR) systems—technology that uses artificial intelligence (AI) and natural language processing to drive popular voice assistants like Siri and similar apps—large hurdles remain, particularly when it comes to gathering fair and accurate datasets upon which to train the AI models.

This problem is even more glaring for populations that can benefit the most from using these advanced technologies for education—young children ages 3 to 8.

To address this challenge, a University of Maryland expert in education policy analysis recently co-hosted an international workshop focused on improving the collection, curation and dissemination of data that is needed for ASR systems used in education.

The International Workshop on Rethinking Children’s Automatic Speech Recognition for Education—held on September 10 at the University at Buffalo—brought together more than 50 researchers, practitioners, funders, developers, and educators to foster real conversations about moving the field forward, says Jing Liu, an associate professor of education policy at the University of Maryland.

“The data problem is real and urgent,” says Liu, who directs the Center for Educational Data Science and Innovation at UMD. “We're facing a massive shortage of children's speech datasets, which creates barriers for developing accurate, fair ASR systems.

Meanwhile, Liu adds, the potential applications in EdTech and educational assessment are mind-blowing—including uses like real-time reading support, authentic language assessment, and truly adaptive learning platforms.