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Abstract
Objective
We demonstrated that it was possible to predict ALS patients’ degree of future speech impairment based on past data. We used longitudinal data from two ALS studies where participants recorded their speech on a daily or weekly basis and provided ALSFRS-R speech subscores on a weekly or quarterly basis (quarter-annually).
Methods
Using their speech recordings, we measured articulatory precision (a measure of the crispness of pronunciation) using an algorithm that analyzed the acoustic signal of each phoneme in the words produced. First, we established the analytical and clinical validity of the measure of articulatory precision, showing that the measure correlated with perceptual ratings of articulatory precision (r = .9). Second, using articulatory precision from speech samples from each participant collected over a 45–90 day model calibration period, we showed it was possible to predict articulatory precision 30–90 days after the last day of the model calibration period. Finally, we showed that the predicted articulatory precision scores mapped onto ALSFRS-R speech subscores.
Results
The mean absolute error was as low as 4% for articulatory precision and 14% for ALSFRS-R speech subscores relative to the total range of their respective scales.
Conclusion
Our results demonstrated that a subject-specific prognostic model for speech predicts future articulatory precision and ALSFRS-R speech values accurately.
Declaration of interest
Dr. Visar Berisha is an Associate Professor at Arizona State University. He is a co-founder of Aural Analytics.
Dr. Julie Liss is a Professor and Associate Dean at Arizona State University. She is a co-founder of Aural Analytics.
Dr. Jeremy Shefner is the Kemper and Ethel Marley Professor and Chair of Neurology at the Barrow Neurological Institute. He is a scientific advisor to Aural Analytics.