Automated Analysis of Fluency Behaviors in Aphasia.

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 comma...

Descripción completa

Detalles Bibliográficos
Publicado en:Journal of Speech, Language & Hearing Research Vol. 67; no. 7; pp. 2333 - 2343
Autores principales: Fromm, Davida, Chern, Steffi, Geng, Zihan, Kim, Mason, Greenhouse, Joel, MacWhinney, Brian
Formato: Artículo
Publicado: American Speech-Language-Hearing Association Jul2024
Materias:
Acceso en línea:Ver este registro en EBSCOhost
fields @attributes:
  recordID: 1
pdfLink:
plink: https://search.ebscohost.com/login.aspx?direct=true&db=ssf&AN=178362741&site=ehost-live
header:
  @attributes:
    shortDbName: ssf
    uiTerm: 178362741
    longDbName: Social Sciences Full Text (H.W. Wilson)
    uiTag: AN
  controlInfo:
    bkinfo:
    jinfo:
      jid:
        10924388
        1SM
      jtl: Journal of Speech, Language & Hearing Research
      issn: 10924388
      maglogo: N
    pubinfo:
      dt: Jul2024
      vid: 67
      iid: 7
      pid: 42
      pub: American Speech-Language-Hearing Association
    artinfo:
      ui:
        178362741
        10.1044/2024_JSLHR-23-00659
      ppf: 2333
      ppct: 10
      formats:
        fmt:
          @attributes:
            type: P
            size: 733KB
      tig:
        atl: Automated Analysis of Fluency Behaviors in Aphasia.
      aug:
        au:
          Fromm, Davida
          Chern, Steffi
          Geng, Zihan
          Kim, Mason
          Greenhouse, Joel
          MacWhinney, Brian
        affil:
          Department of Psychology, Carnegie Mellon University, Pittsburgh, PA.
          Department of Statistics and Data Science, Carnegie Mellon University, Pittsburgh, PA.
      su:
        Evaluation of medical care
        Discourse analysis
        Storytelling
        Analysis of variance
        Language acquisition
        Diagnosis of aphasia
        Data analysis
        Research funding
        Research methodology evaluation
        Phonological awareness
        Stuttering
        Descriptive statistics
        Physiological aspects of speech
        Speech evaluation
        Statistics
        Automation
        Factor analysis
        Confidence intervals
      sug:
        subj:
          Evaluation of medical care
          Discourse analysis
          Storytelling
          Analysis of variance
          Language acquisition
          Diagnosis of aphasia
          Data analysis
          Research funding
          Research methodology evaluation
          Phonological awareness
          Stuttering
          Descriptive statistics
          Physiological aspects of speech
          Speech evaluation
          Statistics
          Automation
          Factor analysis
          Confidence intervals
      ab: 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.
      pubtype: Academic Journal
      doctype: Article
      src: R
    language: English
    refInfo:
    copyright:
      @attributes:
        flag: N
    holdings:
      @attributes:
        islocal: N