Utility of a digital motor speech measurement program using an AI speech recognition module: A pilot study*

This study evaluated the agreement between an AI-based speech recognition module and the acoustic analysis software Praat, focusing on the reliability and interchangeability of the two methods. Maximum phonation time (MPT) and diadochokinetic (DDK) tasks were used to analyze mean differences and limits of agreement. For MPT, the limits of agreement were 0.72 and –0.81, with 93% of the results falling within these limits and a mean difference of –0.04, indicating strong agreement and high accuracy. For the DDK tasks, 91% of the /pʌ/, /tʌ/, and /kʌ/ results fell within the limits, with mean differences of 0.23, 0.17, and 0.23, respectively, demonstrating reliable accuracy. The /pʌtʌkʌ/ task showed 93% of results within the limits, with a mean difference of 0.03, confirming very high agreement and accuracy. These findings suggest that AI-based automatic speech recognition technology has strong potential for clinical acoustic analysis, offfering reliability and possibility of complementing or replacing traditional tools. This study also provides a foundation for developing speech recognition-based learning and therapeutic tools for children with and without speech sound disorders.