Prosody of speech production in latent post-stroke aphasia

TL;DR

This study investigates prosodic speech features in individuals with latent post-stroke aphasia, revealing subtle differences from neurotypical speech and demonstrating the potential of machine learning for diagnosis.

Contribution

It provides new insights into the prosodic characteristics of latent aphasia and introduces a machine learning approach for its reliable classification.

Findings

Differences in prosodic measures between latent aphasia and controls

Prosodic features are significant for distinguishing latent aphasia

Random forest classifier effectively identifies latent aphasia

Abstract

This study explores prosodic production in latent aphasia, a mild form of aphasia associated with left-hemisphere brain damage (e.g. stroke). Unlike prior research on moderate to severe aphasia, we investigated latent aphasia, which can seem to have very similar speech production with neurotypical speech. We analysed the f0, intensity and duration of utterance-initial and utterance-final words of ten speakers with latent aphasia and ten matching controls. Regression models were fitted to improve our understanding of this understudied type of very mild aphasia. The results highlighted varying degrees of differences in all three prosodic measures between groups. We also investigated the diagnostic classification of latent aphasia versus neurotypical control using random forest, aiming to build a fast and reliable tool to assist with the identification of latent aphasia. The random forest analysis also reinforced the significance of prosodic features in distinguishing latent aphasia.