Automating speech screening for children through AI innovation

Speech and language impairments affect over a million children every year, and identifying and treating these conditions early is key to helping these children overcome them. Clinicians struggling with time, resources, and access are in desperate need of tools to make diagnosing speech impairments faster and more accurate.

Marisha Speights, assistant professor at Northwestern University, built a data pipeline to train clinical artificial intelligence tools for childhood speech screening. She will present her work Monday, May 19, at 8:20 a.m. CT as part of the joint 188th Meeting of the Acoustical Society of America and 25th International Congress on Acoustics, running May 18-23.

AI-based speech recognition and clinical diagnostic tools have been in use for years, but these tools are typically trained and used exclusively on adult speech. That makes them unsuitable for clinical work involving children. New AI tools must be developed, but there are no large datasets of recorded child speech for these tools to be trained on, in part because building these datasets is uniquely challenging.

There's a common misconception that collecting speech from children is as straightforward as it is with adults - but in reality, it requires a much more controlled and developmentally sensitive process. Unlike adult speech, child speech is highly variable, acoustically distinct, and underrepresented in most training corpora."

Marisha Speights, Assistant Professor, Northwestern University

To remedy this, Speights and her colleagues began collecting and analyzing large volumes of child speech recordings to build such a dataset. However, they quickly realized a problem: The collection, processing, and annotation of thousands of speech samples is difficult without exactly the kind of automated tools they were trying to build.

"It's a bit of a catch-22," said Speights. "We need automated tools to scale data collection, but we need large datasets to train those tools."

In response, the researchers built a computational pipeline to turn raw speech data into a useful dataset for training AI tools. They collected a representative sample of speech from children across the country, verified transcripts and enhanced audio quality using their custom software, and provided a platform that will enable detailed annotation by experts.

The result is a high-quality dataset that can be used to train clinical AI, giving experts access to a powerful set of tools to make diagnosing speech impairments much easier.

"Speech-language pathologists, health care clinicians and educators will be able to use AI-powered systems to flag speech-language concerns earlier, especially in places where access to specialists is limited," said Speights.