Advancing Diagnostic Tools for Childhood Speech Impairments Through Clinical AI

Speech and language disorders affect over one million children annually, making early detection and intervention crucial for successful therapy outcomes. Healthcare professionals often face challenges such as limited time, resources, and access to specialized services when diagnosing these impairments. To address this gap, researchers are developing innovative AI-based diagnostic tools tailored for pediatric speech assessment.

Marisha Speights, an assistant professor at Northwestern University, presented her team's work at the joint 188th Meeting of the Acoustical Society of America and the 25th International Congress on Acoustics. Her team has created a comprehensive data pipeline to train AI systems specifically for childhood speech screening. While AI tools for adult speech recognition are well-established, they are ill-suited for children due to the significant variability and acoustic differences in young children's speech patterns. This has posed challenges in collecting large, high-quality datasets necessary for training effective AI models.

"Collecting speech data from children requires a more controlled and developmentally sensitive approach than with adults," explains Speights. "Child speech is highly variable, acoustically distinct, and underrepresented in existing datasets." To overcome this, her team has been collecting and analyzing extensive recordings of children's speech to build a specialized dataset. However, manually processing and annotating thousands of samples is labor-intensive and underscores the need for automated solutions.

To solve this, the researchers developed a computational pipeline that transforms raw speech recordings into an analyzable dataset. This pipeline includes verifying transcripts, enhancing audio quality, and providing a platform for expert annotation. The resulting high-quality dataset is a valuable resource to train AI models, which can significantly improve the speed and accuracy of diagnosing speech impairments.

"Speech-language pathologists, clinicians, and educators will be able to leverage AI-powered systems to identify speech concerns earlier, especially in underserved areas where access to specialists is limited," Speights notes. The development of these tools holds the promise of transforming pediatric speech diagnostics by making them more accessible and efficient.

Source: Medical Xpress