-
1
المؤلفون: Sarah Gregory, Nicklas Linz, Alexandra König, Kai Langel, Hannah Pullen, Saturnino Luz, John Harrison, Craig W Ritchie
المساهمون: Spatio-Temporal Activity Recognition Systems (STARS), Inria Sophia Antipolis - Méditerranée (CRISAM), Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)
المصدر: BMJ Open
BMJ Open, 2022, 12, ⟨10.1136/bmjopen-2021-052250⟩
Gregory, S, Linz, N, König, A, Langel, K, Pullen, H, Luz, S, Harrison, J & Ritchie, C W 2022, ' Remote data collection speech analysis and prediction of the identification of Alzheimer’s disease biomarkers in people at risk for Alzheimer’s disease dementia: the Speech on the Phone Assessment (SPeAk) prospective observational study protocol ', BMJ Open, vol. 12, no. 3, pp. e052250 . https://doi.org/10.1136/bmjopen-2021-052250Testمصطلحات موضوعية: [SCCO]Cognitive science, Observational Studies as Topic, Alzheimer Disease, Artificial Intelligence, Surveys and Questionnaires, Humans, Speech, Cognitive Dysfunction, General Medicine, Middle Aged, Biomarkers, Aged, Randomized Controlled Trials as Topic
الوصف: IntroductionIdentifying cost-effective, non-invasive biomarkers of Alzheimer’s disease (AD) is a clinical and research priority. Speech data are easy to collect, and studies suggest it can identify those with AD. We do not know if speech features can predict AD biomarkers in a preclinical population.Methods and analysisThe Speech on the Phone Assessment (SPeAk) study is a prospective observational study. SPeAk recruits participants aged 50 years and over who have previously completed studies with AD biomarker collection. Participants complete a baseline telephone assessment, including spontaneous speech and cognitive tests. A 3-month visit will repeat the cognitive tests with a conversational artificial intelligence bot. Participants complete acceptability questionnaires after each visit. Participants are randomised to receive their cognitive test results either after each visit or only after they have completed the study. We will combine SPeAK data with AD biomarker data collected in a previous study and analyse for correlations between extracted speech features and AD biomarkers. The outcome of this analysis will inform the development of an algorithm for prediction of AD risk based on speech features.Ethics and disseminationThis study has been approved by the Edinburgh Medical School Research Ethics Committee (REC reference 20-EMREC-007). All participants will provide informed consent before completing any study-related procedures, participants must have capacity to consent to participate in this study. Participants may find the tests, or receiving their scores, causes anxiety or stress. Previous exposure to similar tests may make this more familiar and reduce this anxiety. The study information will include signposting in case of distress. Study results will be disseminated to study participants, presented at conferences and published in a peer reviewed journal. No study participants will be identifiable in the study results.
وصف الملف: application/pdf
الوصول الحر: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::ce6d50b44ac615958f2bc509c898ccacTest
https://doi.org/10.1136/bmjopen-2021-052250Test -
2
المؤلفون: Nicklas Linz, Philipp Müller, Insa Kröger, Frans R.J. Verhey, Inez H.G.B. Ramakers, Radia Zeghari, Alexandra König, Johannes Tröger, Hali Lindsay
المصدر: RANLP
مصطلحات موضوعية: Computer science, business.industry, computer.software_genre, Variety (linguistics), language.human_language, Test (assessment), Task (project management), German, language, Verbal fluency test, Screening tool, Artificial intelligence, Cognitive impairment, business, computer, Word (computer architecture), Natural language processing
الوصف: The Semantic Verbal Fluency Task (SVF) is an efficient and minimally invasive speech-based screening tool for Mild Cognitive Impairment (MCI). In the SVF, testees have to produce as many words for a given semantic category as possible within 60 seconds. State-of-the-art approaches for automatic evaluation of the SVF employ word embeddings to analyze semantic similarities in these word sequences. While these approaches have proven promising in a variety of test languages, the small amount of data available for any given language limits the performance. In this paper, we for the first time investigate multilingual learning approaches for MCI classification from the SVF in order to combat data scarcity. To allow for cross-language generalisation, these approaches either rely on translation to a shared language, or make use of several distinct word embeddings. In evaluations on a multilingual corpus of older French, Dutch, and German participants (Controls=66, MCI=66), we show that our multilingual approaches clearly improve over single-language baselines.
الوصول الحر: https://explore.openaire.eu/search/publication?articleId=doi_________::6f46b5be971094a6d07989b35a6bbbb1Test
https://doi.org/10.26615/978-954-452-072-4_095Test -
3
المؤلفون: Dimitrios Kokkinakis, Nicklas Linz, Philippe Robert, Frank Rudzicz, Jan Alexandersson, Bai Li, Alexandra König, Kathleen C. Fraser, Kristina Lundholm Fors
المصدر: NAACL-HLT (1)
مصطلحات موضوعية: Domain adaptation, Training set, business.industry, Computer science, computer.software_genre, medicine.disease, Task (project management), 030507 speech-language pathology & audiology, 03 medical and health sciences, 0302 clinical medicine, medicine, Dementia, Language modelling, Language model, Artificial intelligence, 0305 other medical science, business, Set (psychology), computer, 030217 neurology & neurosurgery, Natural language processing
الوصف: There is growing evidence that changes in speech and language may be early markers of dementia, but much of the previous NLP work in this area has been limited by the size of the available datasets. Here, we compare several methods of domain adaptation to augment a small French dataset of picture descriptions (n = 57) with a much larger English dataset (n = 550), for the task of automatically distinguishing participants with dementia from controls. The first challenge is to identify a set of features that transfer across languages; in addition to previously used features based on information units, we introduce a new set of features to model the order in which information units are produced by dementia patients and controls. These concept-based language model features improve classification performance in both English and French separately, and the best result (AUC = 0.89) is achieved using the multilingual training set with a combination of information and language model features.
الوصول الحر: https://explore.openaire.eu/search/publication?articleId=doi_________::f36e527a739665c9382b8af3ae241d45Test
https://doi.org/10.18653/v1/n19-1367Test -
4
المصدر: PervasiveHealth
مصطلحات موضوعية: Computer science, business.industry, Word error rate, computer.software_genre, medicine.disease, 3. Good health, 03 medical and health sciences, Statistical classification, 0302 clinical medicine, Resource (project management), Pattern recognition (psychology), Classifier (linguistics), medicine, Dementia, Verbal fluency test, 030212 general & internal medicine, Artificial intelligence, Cognitive decline, business, computer, 030217 neurology & neurosurgery, Natural language processing
الوصف: Dementia has a large economic impact on our society as cost-effective population-wide screening for early signs of dementia is still an unsolved medical supply resource problem. A solution should be fast, require a minimum of external material, and automatically indicate potential persons at risk of cognitive decline. Despite encouraging results, leveraging pervasive sensing technologies for automatic dementia screening, there are still two main issues: significant hardware costs or installation efforts and the challenge of effective pattern recognition. Conversely, automatic speech recognition (ASR) and speech analysis have reached sufficient maturity and allow for low-tech remote telephone-based screening scenarios. Therefore, we examine the technologic feasibility of automatically assessing a neuropsychological test---Semantic Verbal Fluency (SVF)--via a telephone-based solution. We investigate its suitability for inclusion into an automated dementia frontline screening and global risk assessment, based on concise telephone-sampled speech, ASR and machine learning classification. Results are encouraging showing an area under the curve (AUC) of 0.85. We observe a relatively low word error rate of 33% despite phone-quality speech samples and a mean age of 77 years of the participants. The automated classification pipeline performs equally well compared to the classifier trained on manual transcriptions of the same speech data. Our results indicate SVF as a prime candidate for inclusion into an automated telephone-screening system.
الوصول الحر: https://explore.openaire.eu/search/publication?articleId=doi_________::db927cef3a693060a6ddc3765aed10acTest
https://doi.org/10.1145/3240925.3240943Test -
5
المؤلفون: P. Robert, Alexandra König, Nicklas Linz, Johannes Tröger, Jan Alexandersson
المصدر: French Journal of Psychiatry. 1:S76
مصطلحات موضوعية: Computer science, business.industry, Word error rate, Cognition, computer.software_genre, Task (project management), Classifier (linguistics), Semantic memory, Verbal fluency test, Artificial intelligence, Cognitive decline, business, computer, Neurocognitive, Natural language processing
الوصف: Semantic verbal fluency (SVF) tests are routinely used in screening for mild cognitive impairment (MCI) and seem to be highly sensitive to cognitive changes [1]. In this task, participants name as many items of a semantic category under a time constraint. SVF can be considered as a multifactorial task, comprising both semantic memory retrieval and executive control processes. However, clinicians measure task performance manually only by summing the number of correct words and errors. More fine-grained variables add valuable information to clinical assessment, but are time-consuming. Therefore, the aim of this study is to investigate whether automatic speech analysis of the SVF could provide these as accurate as manual and thus, support qualitative screening of neurocognitive impairment. Furthermore, we examined the technologic feasibility of automatically assessing the SVF task – via a telephone-based solution for potential remote frontline prescreening of cognitive impairments [2]. We will present SVF data which was collected from 95 older people with MCI (n=47), Alzheimer’s or related dementias (ADRD; n=24) and healthy controls (HC; n=24). Speech was recorded through a mobile tablet device using the built-in microphone. All data was annotated manually and automatically with the named words, clusters and switches. The obtained metrics were validated using a classifier to distinguish HC, MCI and ADRD. Afterwards to simulate telephone conditions, the recordings were downsampled to a lower sampling rate. Automatically extracted clusters and switches were highly correlated (r=0.9) with manually established values, and performed as well on the classification task separating healthy controls from persons with Alzheimer’s (AUC=0.939) and MCI (AUC=0.758) [3]. The downsampled quality obtained similar encouraging results. We observe a relatively low word error rate of 33% despite phone-quality speech samples. The automated classification pipeline performs equally well compared to the classifier trained on manual transcriptions of the same speech data. Our results indicate SVF as a prime candidate for inclusion into an automated telephone-screening system and that it is possible to automate fine-grained analyses of this task for the assessment of cognitive decline.
الوصول الحر: https://explore.openaire.eu/search/publication?articleId=doi_________::f570a363489a16ac7a6174acf5b1a066Test
https://doi.org/10.1016/s2590-2415Test(19)30183-7
