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      Speech in Alzheimer's Disease: Can Temporal and Acoustic Parameters Discriminate Dementia?

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          Aims: The study explores how speech measures may be linked to language profiles in participants with Alzheimer's disease (AD) and how these profiles could distinguish AD from changes associated with normal aging. Methods: We analysed simple sentences spoken by older adults with and without AD. Spectrographic analysis of temporal and acoustic characteristics was carried out using the Praat software. Results: We found that measures of speech, such as variations in the percentage of voice breaks, number of periods of voice, number of voice breaks, shimmer (amplitude perturbation quotient), and noise-to-harmonics ratio, characterise people with AD with an accuracy of 84.8%. Discussion: These measures offer a sensitive method of assessing spontaneous speech output in AD, and they discriminate well between people with AD and healthy older adults. This method of evaluation is a promising tool for AD diagnosis and prognosis, and it could be used as a dependent measure in clinical trials.

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          Most cited references 20

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          Semantic memory impairment in Alzheimer's disease: failure of access or degraded knowledge?

          A battery of neuropsychological tests designed to assess semantic knowledge about the same items both within and across different modalities was administered to a group of 22 patients with dementia of the Alzheimer type (DAT) and 26 matched controls. The DAT patients were impaired on tests of category fluency, picture naming, spoken word-picture matching, picture sorting and generation of verbal definitions. A relative preservation of superordinate knowledge on the sorting and definition tests, as well as a disproportionate reduction in the generation of exemplars from lower order categories was noted. Analysis of the errors made by each patient across the different tests, revealed a significant correspondence between the individual items. These findings offer compelling evidence that the semantic breakdown in DAT is caused by storage degradation.
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            Longitudinal change in language production: effects of aging and dementia on grammatical complexity and propositional content.

            Mixed modeling was used to examine longitudinal changes in linguistic ability in healthy older adults and older adults with dementia. Language samples, vocabulary scores, and digit span scores were collected annually from healthy older adults and semiannually from older adults with dementia. The language samples were scored for grammatical complexity and propositional content. For the healthy group, age-related declines in grammatical complexity and propositional content were observed. The declines were most rapid in the mid 70s. For the group with dementia, grammatical complexity and propositional content also declined over time, regardless of age. Rates of decline were uniform across individuals. These analyses reveal how both grammatical complexity and propositional content are related to late-life changes in cognition in healthy older adults aswell as those with dementia. Alzheimer's disease accelerates this decline, regardless of age.
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              Spoken Language Derived Measures for Detecting Mild Cognitive Impairment.

              Spoken responses produced by subjects during neuropsychological exams can provide diagnostic markers beyond exam performance. In particular, characteristics of the spoken language itself can discriminate between subject groups. We present results on the utility of such markers in discriminating between healthy elderly subjects and subjects with mild cognitive impairment (MCI). Given the audio and transcript of a spoken narrative recall task, a range of markers are automatically derived. These markers include speech features such as pause frequency and duration, and many linguistic complexity measures. We examine measures calculated from manually annotated time alignments (of the transcript with the audio) and syntactic parse trees, as well as the same measures calculated from automatic (forced) time alignments and automatic parses. We show statistically significant differences between clinical subject groups for a number of measures. These differences are largely preserved with automation. We then present classification results, and demonstrate a statistically significant improvement in the area under the ROC curve (AUC) when using automatic spoken language derived features in addition to the neuropsychological test scores. Our results indicate that using multiple, complementary measures can aid in automatic detection of MCI.

                Author and article information

                Dement Geriatr Cogn Disord
                Dementia and Geriatric Cognitive Disorders
                S. Karger AG
                June 2014
                30 January 2014
                : 37
                : 5-6
                : 327-334
                aInstitute of Neurosciences of Castilla y León, University of Salamanca, Salamanca, and bUniversity of Murcia, Murcia, Spain
                Author notes
                *Juan J.G. Meilán, Department of Psychology, Institute of Neuroscience of Castilla y León, University of Salamanca, C/ Pintor Fernando Gallego, 1, ES-37007 Salamanca (Spain), E-Mail
                356726 Dement Geriatr Cogn Disord 2014;37:327-334
                © 2014 S. Karger AG, Basel

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                Page count
                Tables: 2, Pages: 8
                Original Research Article


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