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Automated Analysis of Fluency Behaviors in Aphasia.
Davida Fromm1, Steffi Chern2, Zihan Geng2
1Department of Psychology, Carnegie Mellon University, Pittsburgh, PA.
Summary
Automated language analysis using FLUCALC efficiently measures fluency in aphasia. This tool provides objective data on speech patterns, aiding researchers and clinicians in understanding aphasia.
Area of Science:
- Linguistics
- Neurology
- Speech-Language Pathology
Background:
- Aphasia significantly impacts language fluency.
- Objective and efficient measures are needed to assess fluency in aphasia.
- Automated tools can potentially provide reliable outcome measures.
Purpose of the Study:
- To evaluate the efficacy of the FLUCALC command within the CLAN software.
- To determine if FLUCALC can generate objective outcome measures for fluency in individuals with aphasia.
- To assess the tool's efficiency in analyzing language samples.
Main Methods:
- Utilized the FLUCALC command from CLAN software.
- Analyzed CHAT transcripts from the AphasiaBank database.
- Included language samples from people with aphasia (n=281) and control participants (n=257).
Main Results:
- People with aphasia exhibited reduced word count, slower speech rate, and increased pausing and repetitions compared to controls.
- Specific aphasia types (e.g., Broca's, conduction) showed distinct patterns in fluency measures.
- Principal components analysis identified key factors related to speech quantity, rate, and quality.
- Clustering analysis revealed three participant groups aligning with control, nonfluent, and fluent aphasia types.
Conclusions:
- FLUCALC offers an efficient method for quantifying objective fluency behaviors in aphasia.
- Automated analysis of large language samples provides valuable data for research and clinical practice.
- FLUCALC aids in a deeper understanding and tracking of fluency aspects in aphasia.
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