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

TL;DR

This study validates automated speech and pause measurement algorithms in ALS patients, showing transformer-based models align best with traditional methods, supporting remote disease monitoring and diagnostic tools.

Contribution

It introduces and validates automated speech feature extraction methods using pretrained models for ALS, demonstrating their accuracy compared to semi-automatic analysis.

Findings

Transformer-based models showed highest agreement with gold standards.

Automated methods are effective across different audio qualities and disease severities.

Supports development of remote, automated ALS diagnostic and progression tracking tools.

Abstract

The analysis of speech measures in individuals with amyotrophic lateral sclerosis (ALS) can provide essential information for early diagnosis and tracking disease progression. However, current methods for extracting speech and pause features are manual or semi-automatic, which makes them time-consuming and labour-intensive. The advent of speech-text alignment algorithms provides an opportunity for inexpensive, automated, and accurate analysis of speech measures in individuals with ALS. There is a need to validate speech and pause features calculated by these algorithms against current gold standard methods. In this study, we extracted 8 speech/pause features from 646 audio files of individuals with ALS and healthy controls performing passage reading. Two pretrained forced alignment models - one using transformers and another using a Gaussian mixture / hidden Markov architecture - were used for automatic feature extraction. The results were then validated against semi-automatic speech/pause analysis software, with further subgroup analyses based on audio quality and disease severity. Features extracted using transformer-based forced alignment had the highest agreement with gold standards, including in terms of audio quality and disease severity. This study lays the groundwork for future intelligent diagnostic support systems for clinicians, and for novel methods of tracking disease progression remotely from home.