Developing age-specific protocols for pediatric voice databases in artificial intelligence research

Children's voice and communication abilities evolve with age, necessitating tailored protocols for accurate analysis. Distinct vocal properties and communication styles across developmental stages require specific tasks. The absence of standardized pediatric protocols impedes reliable voice data collection, limits robust acoustic measurements, and hinders AI-driven voice research. This study aims to develop age-specific voice data collection protocols, creating a pediatric voice database to advance research and enhance AI applications.

Protocols were developed iteratively over six months through a multidisciplinary, multi-institutional team involving speech-language pathologists and otolaryngologists. A comprehensive literature review was completed. Emphasis was placed on designing protocols capable of generating high-quality, consistent data for AI modeling while minimizing measurement variability.

Four developmentally appropriate protocols were created for children aged 2 to <4 years, 4 to <6 years, 6 to <10 years, and 10+ years respectively. Each protocol targeted key acoustic parameters while aligning with the linguistic and cognitive abilities of the respective age group. The iterative design incorporated tasks that engaged children while ensuring clinically robust data collection. Standardized protocols were deployed via a user-friendly iPad application, using child-sized headset microphones at fixed distances from the mouth. The protocols were administered to 100 pediatric patients in an otolaryngology clinic between the ages of 2-18.

The development of age specific protocols aims to standardize the collection of pediatric voice and acoustic sound data, which will serve as a valuable resource for advancing artificial intelligence applications within clinical sciences. Future validation will ensure feasibility, adherence, and reliability of data collection to support AI-driven innovations.