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The analysis of speech measures in individuals with amyotrophic lateralsclerosis (ALS) can provide essential information for early diagnosis andtracking disease progression. However, current methods for extracting speechand pause features are manual or semi-automatic, which makes themtime-consuming and labour-intensive. The advent of speech-text alignmentalgorithms provides an opportunity for inexpensive, automated, and accurateanalysis of speech measures in individuals with ALS. There is a need tovalidate speech and pause features calculated by these algorithms againstcurrent gold standard methods. In this study, we extracted 8 speech/pausefeatures from 646 audio files of individuals with ALS and healthy controlsperforming passage reading. Two pretrained forced alignment models - one usingtransformers and another using a Gaussian mixture / hidden Markov architecture- were used for automatic feature extraction. The results were then validatedagainst semi-automatic speech/pause analysis software, with further subgroupanalyses based on audio quality and disease severity. Features extracted usingtransformer-based forced alignment had the highest agreement with goldstandards, including in terms of audio quality and disease severity. This studylays the groundwork for future intelligent diagnostic support systems forclinicians, and for novel methods of tracking disease progression remotely fromhome.

Concurrent Validity of Automatic Speech and Pause Measures During Passage Reading in ALS