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. 2024 Jul 9;67(7):2333-2342.
doi: 10.1044/2024_JSLHR-23-00659. Epub 2024 Jun 14.

Automated Analysis of Fluency Behaviors in Aphasia

Davida Fromm  1 , Steffi Chern  2 , Zihan Geng  2 , Mason Kim  2 , Joel Greenhouse  2 , Brian MacWhinney  1
Affiliations

Automated Analysis of Fluency Behaviors in Aphasia

Davida Fromm et al. J Speech Lang Hear Res. .

Abstract

Purpose: This study explored the use of an automated language analysis tool, FLUCALC, for measuring fluency in aphasia. The purpose was to determine whether CLAN's FLUCALC command could produce efficient, objective outcome measures for salient aspects of fluency in aphasia.

Method: The FLUCALC command was used on CHAT transcripts of Cinderella stories from people with aphasia (PWA; n = 281) and controls (n = 257) in the AphasiaBank database.

Results: PWA produced significantly fewer total words, fewer words per minute, more pausing, more repetitions, more revisions, and more phonological fragments than controls, with only one exception: The Wernicke's group was similar to the control group in percentage of filled pauses. Individuals with Broca's aphasia had significantly longer inter-utterance pauses and fewer total words than all other aphasia groups. Both the Broca's and conduction aphasia groups had higher percentages of phrase repetitions than the NABW (NotAphasicByWAB) group. The conduction aphasia group also had a higher percentage of phrase revisions than the NABW and the anomic aphasia groups. Principal components analysis revealed two principal components that accounted for around 60% of the variance and related to quantity of output, rate of speech, and quality of output. The Gaussian mixture models showed that the participants clustered in three groups, which corresponded predominantly to the controls, the nonfluent aphasia group, and the remaining aphasia groups (all classically fluent aphasia types).

Conclusions: FLUCALC is an efficient way to measure objective fluency behaviors in language samples in aphasia. Automated analyses of objective fluency behaviors on large samples of adults with and without aphasia can produce measures that can be used by researchers and clinicians to better understand and track salient aspects of fluency in aphasia.

Supplemental material: https://doi.org/10.23641/asha.25979863.

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Figures

Figure 1.
Figure 1.
Principal components analysis scatterplot by aphasia groups. PC1 = first principal component; PC2 = second principal component; WAB = Western Aphasia Battery.
Figure 2.
Figure 2.
Clustering with aphasia group labels. PC1 = first principal component; PC2 = second principal component; WAB = Western Aphasia Battery.
Figure 3.
Figure 3.
Clustering without aphasia group labels. PC1 = first principal component; PC2 = second principal component.
See this image and copyright information in PMC

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