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acoustic_analysis_of_speech_patterns._a_marker_for_dementia [2014/03/15 12:29] (current)
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 +Anna Barney[1],Dragana Nikolic[1], Manuela Bung[1]  & Peter Garrard [2]
 +**Acoustic analysis of speech patterns. A  marker for dementia**
 +[1] Institute of Sound and Vibration Research, University of Southampton, Southampton, UK
 +[2] St George’s, University or London, London, UK
 +Incidence of dementia is increasing, due in part to increased life-expectancy, and the cost of care is set to increase correspondingly. This has generated an increase in research activity surrounding neuro-degenerative diseases focussed on a search for pharmacological approaches to modify or reverse the progress of disease as well as on basic science to understand the disease processes. In parallel there is an increased interest in finding biomarkers for disease progress. To date the biomarkers identified give a good global estimate of the health status of a patient, but are either insensitive to short term changes or unsuitable for frequent testing (e.g. sampling of cerebro-spinal fluid in Alzheimer’s disease). It is therefore necessary to search, in parallel, for functional markers of patient behaviour and performance that are sensitive to disease progress on a more short term basis, that can be measured non-intrusively, that continue to give reliable and valid data when a patient loses mental capacity and which are not subject to a learning effect. The speech patterns of patients with dementia have the potential to meet all the requirements of such a functional marker. 
 +Clinicians and carers report anecdotally that speech patterns are characteristic of different types of dementia, for instance perseverative repetition is commonly reported in Alzheimer’s disease; difficulties in social interaction through speech, reduced semantic ability and aphasia are all seen in different forms of fronto-temporal dementia. Further, language impairment seems to increase with disease progression.
 +This paper will discuss the prospect of using characteristic speech patterns as functional markers and will report results from pilot studies with a system for recording speech in a non-intrusive manner that respects the privacy of patients and their conversational partners while automatically detecting repetition of words or phrases. Initial trials using actors suggest that abnormal patterns can be detected with good reliability when the parameters of the system are suitably tuned.
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