A Literature Review on Artificial Intelligence-Based Speech Intelligibility Assessment in Motor Speech Disorders

This scoping review aimed to examine the methodological characteristics and performance of artificial intelligence (AI)-based speech intelligibility assessment in individuals with acquired motor speech disorders. It also evaluated the clinical applicability of AI-based assessment by reviewing the relationship between AI-based assessments and human perceptual ratings. Four international databases (PubMed, IEEE Xplore, Web of Science, and ScienceDirect) and three Korean databases (KISS, DBPIA, and RISS) were searched up to March 2026. Fifteen studies were selected after applying predefined exclusion criteria. The selected studies were classified and synthesized descriptively without risk-of-bias assessment. 1) Reading tasks were the most frequently used speech task across the 15 included studies, regardless of the data collection method. 2) A wide range of automatic speech recognition systems (e.g., Google Cloud ASR API; Google LLC, Mountain View, CA, USA) and machine learning and deep learning models (e.g., bidirectional long-short term memory, convolutional neural networks, support vector machines, and linear regression) were used, with no single approach predominating. 3) Many studies reported strong correlations or high levels of agreement between AI-based assessments and human perceptual ratings, whereas some reported weak correlations or nonlinear relationships. The included studies generally reported positive associations between AI-based and perceptual intelligibility measures, although the performance metrics and models varied considerably across studies. Therefore, the findings should be interpreted with caution in clinical applications.