  We propose a cloud-based multimodal dialog platform for the remote assessment
and monitoring of Amyotrophic Lateral Sclerosis (ALS) at scale. This paper
presents our vision, technology setup, and an initial investigation of the
efficacy of the various acoustic and visual speech metrics automatically
extracted by the platform. 82 healthy controls and 54 people with ALS (pALS)
were instructed to interact with the platform and completed a battery of
speaking tasks designed to probe the acoustic, articulatory, phonatory, and
respiratory aspects of their speech. We find that multiple acoustic (rate,
duration, voicing) and visual (higher order statistics of the jaw and lip)
speech metrics show statistically significant differences between controls,
bulbar symptomatic and bulbar pre-symptomatic patients. We report on the
sensitivity and specificity of these metrics using five-fold cross-validation.
We further conducted a LASSO-LARS regression analysis to uncover the relative
contributions of various acoustic and visual features in predicting the
severity of patients' ALS (as measured by their self-reported ALSFRS-R scores).
Our results provide encouraging evidence of the utility of automatically
extracted audiovisual analytics for scalable remote patient assessment and
monitoring in ALS.
