Objective automated analysis of natural language: The Fluency Profiling System as a measure of the efficiency of dynamic language networks.
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1
University of Notre Dame, Australia
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2
University of Western Australia, Australia
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3
University of Melbourne, Australia
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4
University of Adelaide, Australia
Recent advances in neuro-imaging have provided biological confirmation of the presence of large-scale networks underpinning language processing (Thompson & den Ouden, 2008). Fluent effortless speech production is thought to reflect optimum network function. Hird and Kirsner (2010) demonstrated in three diverse aphasic speakers that the Fluency Profiling System (Little, Oehmen, Dunn, Hird & Kirsner (2012) provides a powerful, objective and sensitive profile of fluency in natural speech samples.
Discourse samples from the Cinderella story were selected from the AphasiaBank repository (McWhinney et al., 2011) if they met an empirically defined Signal: Noise ratio. To date a total of 28 cases have been analysed: 18 with a diagnosis of Broca’s aphasia and 10 controls. Dependent measures of long pauses, short pauses and speech segment durations (ln) for each aphasic were converted to z scores by comparison with the control group. All of the Broca’s aphasics showed shorter mean speech segment durations than the control group. Nine cases produced significantly longer mean short pause durations.
The results demonstrate that the FPS is a sensitive tool for characterisation of cognitive and motor processes associated with the impact of brain impairment on spontaneous speaking. It provides inferential statistics that quantify function across cognitive and motor domains beyond those provided by traditional categorical or model based diagnostic tools.
References
Hird K, Kirsner K (2010) Objective measurement of fluency in natural language production: A dynamic systems approach. Journal of Neurolinguistics. 23 (5): Special Issue: 518-530.
Little, D., Oehmen, R., Dunn, J, Hird, K & Kirsner, K. (2012) Fluency Profiling System: An automated system for analyzing the temporal properties of speech. Behavioural Research. DOI 10.3758/s13428-012-0222-0
MacWhinney, B., Fromm, D., Forbes, M & Holland, A. (2011). AphasiaBank: Methods for studying discourse. Aphasiology, 25, 1286-1307
Thompson, C. K., & den Ouden, D. B. (2008). Neuroimaging and recovery of language in aphasia. Current Neurology and Neuroscience, 8(6), 475–483.
Keywords:
language measurement,
fluency,
Aphasia,
speech science,
validation of methods,
natural language
Conference:
ACNS-2012 Australasian Cognitive Neuroscience Conference, Brisbane, Australia, 29 Nov - 2 Dec, 2012.
Presentation Type:
Poster Presentation
Topic:
Language
Citation:
Hird
KM,
Kirsner
K,
Little
D,
Oehmen
R and
Dunn
J
(2012). Objective automated analysis of natural language: The Fluency Profiling System as a measure of the efficiency of dynamic language networks..
Conference Abstract:
ACNS-2012 Australasian Cognitive Neuroscience Conference.
doi: 10.3389/conf.fnhum.2012.208.00045
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Received:
25 Oct 2012;
Published Online:
07 Nov 2012.
*
Correspondence:
Prof. Kathryn M Hird, University of Notre Dame, Fremantle, WA, Australia, Kathryn.Hird@nd.edu.au