Comparison of automated and manual measures of speaking rate in individuals with dysarthria

Speech patterns in speakers with dysarthria exhibit a broad range of prosodic and articulatory variations, potentially posing a significant challenge for automated speech recognition systems. This study seeks to address a gap in the literature by exploring a fully automated approach to determine speaking rates in English-speaking individuals with dysarthria. This research aims to elucidate whether a syllable-based automated method yields results akin to manual processes when calculating speaking rates across diverse groups [i.e., individuals with dysarthria secondary to multiple sclerosis (MS) or Parkinson’s disease (PD), and healthy controls]. Moreover, this study examines the performance of an automated method across three distinct speech tasks: isolated sentences, paragraph reading, and spontaneous speech. The methodology involves adjusting parameters within a version of a published script specifically designed for calculating speaking rate through syllable detection (Praat Script Syllable Nuclei; de Jong et al., 2021). Using a sample of 60 speakers (20 MS, 20 PD, and 20 healthy controls), this study aims to evaluate the accuracy and validity of an automated script in calculating speaking rate. Ultimately, these findings are intended to guide speaking rate measurement of dysarthria in research and clinical practice.