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      A Speech Recognition-based Solution for the Automatic Detection of Mild Cognitive Impairment from Spontaneous Speech

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          Abstract

          Background:

          Even today the reliable diagnosis of the prodromal stages of Alzheimer’s disease (AD) remains a great challenge. Our research focuses on the earliest detectable indicators of cognitive de-cline in mild cognitive impairment (MCI). Since the presence of language impairment has been reported even in the mild stage of AD, the aim of this study is to develop a sensitive neuropsychological screening method which is based on the analysis of spontaneous speech production during performing a memory task. In the future, this can form the basis of an Internet-based interactive screening software for the recognition of MCI.

          Methods:

          Participants were 38 healthy controls and 48 clinically diagnosed MCI patients. The provoked spontaneous speech by asking the patients to recall the content of 2 short black and white films (one direct, one delayed), and by answering one question. Acoustic parameters (hesitation ratio, speech tempo, length and number of silent and filled pauses, length of utterance) were extracted from the recorded speech sig-nals, first manually (using the Praat software), and then automatically, with an automatic speech recogni-tion (ASR) based tool. First, the extracted parameters were statistically analyzed. Then we applied machine learning algorithms to see whether the MCI and the control group can be discriminated automatically based on the acoustic features.

          Results:

          The statistical analysis showed significant differences for most of the acoustic parameters (speech tempo, articulation rate, silent pause, hesitation ratio, length of utterance, pause-per-utterance ratio). The most significant differences between the two groups were found in the speech tempo in the delayed recall task, and in the number of pauses for the question-answering task. The fully automated version of the analysis process – that is, using the ASR-based features in combination with machine learning - was able to separate the two classes with an F1-score of 78.8%.

          Conclusion:

          The temporal analysis of spontaneous speech can be exploited in implementing a new, auto-matic detection-based tool for screening MCI for the community.

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          Most cited references 45

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          Diagnostic and statistical manual of mental disorders

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            Language performance in Alzheimer's disease and mild cognitive impairment: a comparative review.

            Mild cognitive impairment (MCI) manifests as memory impairment in the absence of dementia and progresses to Alzheimer's disease (AD) at a rate of around 15% per annum, versus 1-2% in the general population. It thus constitutes a primary target for investigation of early markers of AD. Language deficits occur early in AD, and performance on verbal tasks is an important diagnostic criterion for both AD and MCI. We review language performance in MCI, compare these findings to those seen in AD, and identify the primary issues in understanding language performance in MCI and selecting tasks with diagnostic and prognostic value.
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              Innovative diagnostic tools for early detection of Alzheimer's disease.

              Current state-of-the-art diagnostic measures of Alzheimer's disease (AD) are invasive (cerebrospinal fluid analysis), expensive (neuroimaging) and time-consuming (neuropsychological assessment) and thus have limited accessibility as frontline screening and diagnostic tools for AD. Thus, there is an increasing need for additional noninvasive and/or cost-effective tools, allowing identification of subjects in the preclinical or early clinical stages of AD who could be suitable for further cognitive evaluation and dementia diagnostics. Implementation of such tests may facilitate early and potentially more effective therapeutic and preventative strategies for AD. Before applying them in clinical practice, these tools should be examined in ongoing large clinical trials. This review will summarize and highlight the most promising screening tools including neuropsychometric, clinical, blood, and neurophysiological tests.
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                Author and article information

                Journal
                Curr Alzheimer Res
                Curr Alzheimer Res
                CAR
                Current Alzheimer Research
                Bentham Science Publishers
                1567-2050
                1875-5828
                2018
                2018
                : 15
                : 2
                : 130-138
                Affiliations
                [a ]Linguistics Department, University of Szeged , Szeged, , Hungary;
                [b ]Research Institute for Linguistics, Hungarian Academy of Sciences , Budapest, , Hungary;
                [c ] MTA-SZTE Research Group on Artificial Intelligence , Szeged, , Hungary;
                [d ]Department of Psychiatry, University of Szeged , Szeged, , Hungary
                Author notes
                [* ]Address correspondence to this author at the MTA-SZTE Research Group on Artificial Intelligence, Szeged, Hungary; Tel./Fax: ++36-62-544142, ++36-62-54-6737; E-mail: tothl@ 123456inf.u-szeged.hu
                Article
                CAR-15-130
                10.2174/1567205014666171121114930
                5815089
                29165085
                © 2018 Bentham Science Publishers

                This is an open access article licensed under the terms of the Creative Commons Attribution-Non-Commercial 4.0 International Public License (CC BY-NC 4.0) ( https://creativecommons.org/licenses/by-nc/4.0/legalcode), which permits unrestricted, non-commercial use, distribution and reproduction in any medium, provided the work is properly cited.

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