Preprint manuscript, data files, and complete analytic code for: Nightingale, C., Swartz, M. T., Ramig, L. O., & McAllister, T. (2020). Using crowdsourced listeners’ ratings to measure speech changes in hypokinetic dysarthria: A proof-of-concept study. *American Journal of Speech-Language Pathology.* https://doi.org/10.1044/2019_AJSLP-19-00162 **Purpose:** Interventions for speech disorders aim to produce changes that are not only acoustically measurable or perceptible to trained professionals but are also apparent to naïve listeners. Due to challenges associated with obtaining ratings from suitably large listener samples, though, few studies currently evaluate speech interventions by this criterion. Online crowdsourcing technologies could enhance the measurement of intervention effects by making it easier to obtain real-world listeners’ ratings. **Method:** Stimuli, drawn from a published study (Sapir, Spielman, Ramig, Story, & Fox, 2007), were words produced by individuals who received intensive treatment (LSVT LOUD) for hypokinetic dysarthria secondary to Parkinson Disease. Thirty-six online naïve listeners heard randomly ordered pairs of words elicited pre- and post-treatment and reported which they perceived as “more clearly articulated.” **Results:** Mixed-effects logistic regression indicated that words elicited post-treatment were significantly more likely to be rated “more clear.” Across individuals, acoustically measured magnitude of change was significantly correlated with pre-post difference in listener ratings. **Conclusions:** These results partly replicate the findings of Sapir et al. (2007) and demonstrate that their acoustically measured changes are detectable by everyday listeners. This supports the viability of using crowdsourcing to obtain more functionally relevant measures of change in clinical speech samples. ***Acknowledgments**: The current study was supported by NIH R41 DC016778 (PI: McAllister); data collection for the original study (Sapir et al., 2007) was supported by NIH R01 DC01150. The authors gratefully acknowledge Shimon Sapir, Jennifer L. Spielman, Brad H. Story, and Cynthia Fox for generously agreeing to share the materials from their original study. We also thank Daniel Szeredi for coding support, and all participants, both in Sapir et al. (2007) and on Amazon Mechanical Turk, for their time and effort.*