Automated speech analytics in ALS: higher sensitivity of digital articulatory precision over the ALSFRS-R

Abstract

Objective: Although studies have shown that digital measures of speech detected ALS speech impairment and correlated with the ALSFRS-R speech item, no study has yet compared their performance in detecting speech changes. In this study, we compared the performances of the ALSFRS-R speech item and an algorithmic speech measure in detecting clinically important changes in speech. Importantly, the study was part of a FDA submission which received the breakthrough device designation for monitoring ALS; we provide this paper as a roadmap for validating other speech measures for monitoring disease progression. Methods: We obtained ALSFRS-R speech subscores and speech samples from participants with ALS. We computed the minimum detectable change (MDC) of both measures; using clinician-reported listener effort and a perceptual ratings of severity, we calculated the minimal clinically important difference (MCID) of each measure with respect to both sets of clinical ratings. Results: For articulatory precision, the MDC (.85) was lower than both MCID measures (2.74 and 2.28), and for the ALSFRS-R speech item, MDC (.86) was greater than both MCID measures (.82 and .72), indicating that while the articulatory precision measure detected minimal clinically important differences in speech, the ALSFRS-R speech item did not. Conclusion: The results demonstrate that the digital measure of articulatory precision effectively detects clinically important differences in speech ratings, outperforming the ALSFRS-R speech item. Taken together, the results herein suggest that this speech outcome is a clinically meaningful measure of speech change.

Keywords:

• Speech analytics
• digital measures
• validation
• ALS
• disease progression

Declaration of interests

V. Berisha, J. Liss, G. Stegmann, C. Krantsevich, M. Bartlett, J. Shefner, and S. Rutkove have no relevant conflicts of interest to disclose. K. Kawabata, S. Charles, and T. Talkar are employees at Linus Health.

Additional information

Funding

This work was supported by NIH SBIR (1R43DC017625-01), NSF SBIR (1853247), NIH R01 (5R01DC006859-13), and ALS Finding a Cure Grant.