R. Bateman, C. Xiong, . Benzinger, . Tl, . Fagan et al., Clinical and Biomarker Changes in Dominantly Inherited Alzheimer's Disease, New England Journal of Medicine, vol.367, issue.9, pp.795-804, 2012.
DOI : 10.1056/NEJMoa1202753

J. Langbaum, . Fleisher, . As, K. Chen, N. Ayutyanont et al., Ushering in the study and treatment of preclinical Alzheimer disease, Nature Reviews Neurology, vol.33, issue.7, pp.371-381, 2013.
DOI : 10.1016/j.neurobiolaging.2010.04.007

R. Sperling, . Aisen, . Ps, L. Beckett, . Bennett et al., Toward defining the preclinical stages of Alzheimer's Disease: recommendations from the National Institute on Aging-Alzheimer's Association Workgroups on Diagnostic Guidelines for Alzheimer's Disease

, Alzheimers Dement, vol.7, issue.3, pp.280-292, 2011.

S. Sindi, F. Mangialasche, and K. , Advances in the prevention of Alzheimer's Disease, F1000Prime Reports, vol.7, p.50, 2015.
DOI : 10.12703/P7-50

M. Prince, A. Comas-herrera, M. Knapp, M. Guerchet, and K. , M: World Alzheimer Report 2016 Improving healthcare for people living with dementia. Coverage, Quality and Costs now and in the Future

C. Laske, . Sohrabi, . Hr, S. Frost, K. Lopez-de-ipina et al., Innovative diagnostic tools for early detection of Alzheimer's disease, Alzheimer's & Dementia, vol.11, issue.5, pp.561-578, 2015.
DOI : 10.1016/j.jalz.2014.06.004

P. Snyder, K. Kahle-wrobleski, S. Brannan, . Miller, . Ds et al., Assessing cognition and function in Alzheimer's disease clinical trials: Do we have the right tools?, Alzheimer's & Dementia, vol.10, issue.6, pp.853-860, 2014.
DOI : 10.1016/j.jalz.2014.07.158

J. Tröger, N. Linz, J. Alexandersson, A. König, and . Robert, Automated speechbased screening for Alzheimer's Disease in a care service scenario, Proceedings of the 11th EAI International Conference on Pervasive Computing Technologies for Healthcare, 2017.

A. König, A. Satt, A. Sorin, R. Hoory, O. Toledo-ronen et al., Automatic speech analysis for the assessment of patients with predementia and Alzheimer's disease, Alzheimer's & Dementia: Diagnosis, Assessment & Disease Monitoring, vol.1, issue.1, pp.112-124, 2015.
DOI : 10.1016/j.dadm.2014.11.012

A. Satt, R. Hoory, A. König, P. Aalten, and R. , PH: Speech-based automatic and robust detection of very early dementia, Proceedings of the Annual Conference of the International Speech Communication Association, INTER- SPEECH, pp.2538-2542, 2014.

I. Hoffmann, D. Nemeth, C. Dye, M. Pákáski, T. Irinyi et al., Temporal parameters of spontaneous speech in Alzheimer's disease, International Journal of Speech-Language Pathology, vol.112, issue.9, pp.29-34, 2010.
DOI : 10.1007/s00702-004-0215-y

B. Roark, M. Mitchell, J. Hosom, K. Hollingshead, and K. , Spoken Language Derived Measures for Detecting Mild Cognitive Impairment, Proceedings, pp.2081-2090, 2011.
DOI : 10.1109/TASL.2011.2112351

K. Lopez-de-ipina, U. Martinez-de-lizarduy, P. Calvo, J. Mekyskac, B. Beitiad et al., Advances on Automatic Speech Analysis for Early Detection of Alzheimer Disease: A Non-linear Multi-task Approach, Current Alzheimer Research, vol.15, issue.2, pp.139-148, 2017.
DOI : 10.2174/1567205014666171120143800

L. Tóth, I. Hoffmann, G. Gosztolyac, V. Vinczec, G. Szatlóczkid et al., A Speech Recognition-based Solution for the Automatic Detection of Mild Cognitive Impairment from Spontaneous Speech, Current Alzheimer Research, vol.15, issue.2, pp.130-168, 2018.
DOI : 10.2174/1567205014666171121114930

A. König, A. Satt, A. Sorin, R. Hoory, A. Derreumaux et al., PH: Use of speech analyses within a Mobile Application for the assessment of cognitive impairment in elderly people, Curr Alzheimer Res, vol.15, issue.2, pp.120-129, 2018.

K. Fraser, J. Meltzer, and . Rudzicz, Linguistic Features Identify Alzheimer???s Disease in Narrative Speech, Journal of Alzheimer's Disease, vol.63, issue.Pt 11, pp.407-422, 2015.
DOI : 10.1006/brln.1997.1923

K. Lopez-de-ipiña, J. Alonso, C. Travieso, J. Solé-casals, H. Egiraun et al., On the Selection of Non-Invasive Methods Based on Speech Analysis Oriented to Automatic Alzheimer Disease Diagnosis, Sensors, vol.79, issue.5, pp.6730-6745, 2013.
DOI : 10.1080/01621459.1984.10478083

J. Meilan, F. Martinez-sanchez, J. Carro, N. Carcavilla, and O. Ivanova, Voice Markers of Lexical Access in Mild Cognitive Impairment and Alzheimer's Disease, Current Alzheimer Research, vol.15, issue.2, pp.111-119, 2018.
DOI : 10.2174/1567205014666170829112439

L. Tóth, G. Gosztolya, . Vinczev, I. Hoffmann, G. Szatlóczki et al., Automatic detection of mild cognitive impairment from spontaneous speech using ASR, Proceedings: Interspeech 2015 Tutorials & Main Conference

G. Szatloczki, I. Hoffmann, V. Vincze, J. Kalman, and M. Pakaski, Speaking in Alzheimer???s Disease, is That an Early Sign? Importance of Changes in Language Abilities in Alzheimer???s Disease, Frontiers in Aging Neuroscience, vol.8, p.195, 2015.
DOI : 10.1371/journal.pone.0066367

E. Mioshi, K. Dawson, J. Mitchell, R. Arnold, and J. Hodges, The Addenbrooke's Cognitive Examination Revised (ACE-R): a brief cognitive test battery for dementia screening, International Journal of Geriatric Psychiatry, vol.6, issue.11, pp.1078-1085, 2006.
DOI : 10.1212/WNL.51.6.1546

J. Peter, J. Kaiser, V. Landerer, L. Kostering, C. Kaller et al., Category and design fluency in mild cognitive impairment: Performance, strategy use, and neural correlates, Neuropsychologia, vol.93, pp.21-29, 2016.
DOI : 10.1016/j.neuropsychologia.2016.09.024

S. Canning, L. Leach, D. Stuss, L. Ngo, . Black et al., Diagnostic utility of abbreviated fluency measures in Alzheimer disease and vascular dementia, Neurology, vol.62, issue.4, pp.556-562, 2004.
DOI : 10.1212/WNL.62.4.556

C. Marczinski and K. , Category and letter fluency in semantic dementia, primary progressive aphasia, and Alzheimer???s disease, Brain and Language, vol.97, issue.3, pp.258-265, 2006.
DOI : 10.1016/j.bandl.2005.11.001

L. Clark, M. Gatz, L. Zheng, Y. Chen, C. Mccleary et al., Longitudinal Verbal Fluency in Normal Aging, Preclinical, and Prevalent Alzheimer???s Disease, American Journal of Alzheimer's Disease & Other Dementiasr, vol.24, issue.6, pp.461-468, 2009.
DOI : 10.1016/j.cortex.2007.08.019

URL : http://europepmc.org/articles/pmc2824246?pdf=render

J. Henry, J. Crawford, and L. Phillips, Verbal fluency performance in dementia of the Alzheimer???s type: a meta-analysis, Neuropsychologia, vol.42, issue.9, pp.1212-1222, 2004.
DOI : 10.1016/j.neuropsychologia.2004.02.001

S. Pakhomov, L. Eberly, and . Knopman, Characterizing cognitive performance in a large longitudinal study of aging with computerized semantic indices of verbal fluency, Neuropsychologia, vol.89, pp.42-56, 2016.
DOI : 10.1016/j.neuropsychologia.2016.05.031

N. Raoux, H. Amieva, L. Goff, M. Auriacombe, S. Carcaillon et al., Clustering and switching processes in semantic verbal fluency in the course of Alzheimer's disease subjects: Results from the PAQUID longitudinal study, Cortex, vol.44, issue.9, pp.1188-1196, 2008.
DOI : 10.1016/j.cortex.2007.08.019

S. Auriacombe, N. Lechevallier, H. Amieva, S. Harston, N. Raoux et al., A Longitudinal Study of Quantitative and Qualitative Features of Category Verbal Fluency in Incident Alzheimer???s Disease Subjects: Results from the PAQUID Study, Dementia and Geriatric Cognitive Disorders, vol.16, issue.4, pp.260-266, 2006.
DOI : 10.1001/jama.275.7.528

A. Costa, T. Bak, P. Caffarra, C. Caltagirone, M. Ceccaldi et al., The need for harmonisation and innovation of neuropsychological assessment in neurodegenerative dementias in Europe: consensus document of the Joint Program for Neurodegenerative Diseases Working Group, Alzheimer's Research & Therapy, vol.30, issue.4 Suppl 3, p.27, 2017.
DOI : 10.1159/000135644

P. Gruenewald, . Lockhead, and . Gr, The free recall of category examples., Journal of Experimental Psychology: Human Learning & Memory, vol.6, issue.3, pp.225-240, 1980.
DOI : 10.1037/0278-7393.6.3.225

A. K. Troyer, M. Moscovitch, and G. Winocur, Clustering and switching as two components of verbal fluency: Evidence from younger and older healthy adults., Neuropsychology, vol.11, issue.1, pp.138-146, 1997.
DOI : 10.1037/0894-4105.11.1.138

A. Troyer, M. Moscovitch, G. Winocur, L. Leach, and . Freedman, Clustering and switching on verbal fluency tests in Alzheimer's and Parkinson's disease, Journal of the International Neuropsychological Society, vol.4, issue.2, pp.137-143, 1998.
DOI : 10.1017/S1355617798001374

K. Murphy, . Rich, and T. Jb, AK: Verbal fluency patterns in amnestic mild cognitive impairment are characteristic of Alzheimer's type dementia, J Int Neuropsychol Soc, vol.12, issue.4, pp.570-574, 2006.

R. Gomez, . White, and . Da, Using verbal fluency to detect very mild dementia of the Alzheimer type, Archives of Clinical Neuropsychology, vol.21, issue.8, pp.771-775, 2006.
DOI : 10.1016/j.acn.2006.06.012

K. Mueller, R. Koscik, A. Larue, . Clark, . Lr et al., Verbal Fluency and Early Memory Decline: Results from the Wisconsin Registry for Alzheimer's Prevention, Archives of Clinical Neuropsychology, vol.30, issue.5, p.448, 2015.
DOI : 10.1016/j.jalz.2013.05.1769

D. Woods, J. Wyma, . Herron, . Tj, . Yund et al., Computerized Analysis of Verbal Fluency: Normative Data and the Effects of Repeated Testing, Simulated Malingering, and Traumatic Brain Injury, PLOS ONE, vol.9, issue.3, pp.1-37, 2016.
DOI : 10.1371/journal.pone.0166439.s003

D. Clark, . Mclaughlin, . Pm, E. Woo, K. Hwang et al., LG: Novel verbal fluency scores and structural brain imaging for prediction of cognitive outcome in mild cognitive impairment, Alzheimers Dement (Amst), vol.2, pp.113-122, 2016.

S. Pakhomov and H. , A computational linguistic measure of clustering behavior on semantic verbal fluency task predicts risk of future dementia in the Nun Study, Cortex, vol.55, pp.97-106, 2014.
DOI : 10.1016/j.cortex.2013.05.009

K. Ledoux, . Vannorsdall, . Td, . Pickett, . Ej et al., Capturing additional information about the organization of entries in the lexicon from verbal fluency productions, Journal of Clinical and Experimental Neuropsychology, vol.41, issue.6, pp.205-220, 2014.
DOI : 10.3758/BF03200763

E. Gabrilovich and M. , Wikipedia-based Semantic Interpretation for Natural Language Processing, Journal of Artificial Intelligence Research, vol.34, issue.1, pp.443-498, 2009.
DOI : 10.1613/jair.2669

T. Mikolov, I. Sutskever, K. Chen, . Corrado, and D. Gs, J: Distributed representations of words and phrases and their compositionality, Ad-vances in Neural Information Processing Systems, pp.3111-3119, 2013.

N. Linz, J. Tröger, J. Alexandersson, and K. , A: Using neural word embeddings in the analysis of the clinical semantic verbal fluency task In press, Proceedings of the 12th International Conference on Computational Semantics (IWCS), 2017.

, World Health Organization, 1992. The ICD-10 classification of mental and behavioural disorders: clinical descriptions and diagnostic guidelines

M. Folstein, . Folstein, . Se, . Mchugh, and . Pr, ???Mini-mental state???, Journal of Psychiatric Research, vol.12, issue.3, pp.189-198, 1975.
DOI : 10.1016/0022-3956(75)90026-6

O. Bryant, . Se, . Lacritz, . Lh, J. Hall et al., CM: Validation of the new interpretive guidelines for the clinical dementia rating scale sum of boxes score in the National Alzheimer's Coordinating Center Database, Archives of Neurology, vol.67, issue.6, pp.746-749, 2010.

B. Macwhinney, The CHILDES project: tools for analyzing talk, Child Language Teaching and Therapy, vol.8, issue.2, 1991.
DOI : 10.1177/026565909200800211

P. Boersma and W. , D: Praat, a system for doing phonetics by computer, Glot international, vol.5, pp.341-345, 2001.

M. Baroni, S. Bernardini, A. Ferraresi, and . Zanchetta, The WaCky wide web: a collection of very large linguistically processed web-crawled corpora, Language Resources and Evaluation, vol.10, issue.4, pp.209-226, 2009.
DOI : 10.1007/978-94-010-0844-0

A. St-hilaire, C. Hudon, . Vallet, . Gt, L. Bherer et al., Normative data for phonemic and semantic verbal fluency test in the adult French???Quebec population and validation study in Alzheimer???s disease and depression, The Clinical Neuropsychologist, vol.24, issue.7, pp.30-1126, 2016.
DOI : 10.1159/000079198

URL : https://hal.archives-ouvertes.fr/hal-01643613

C. Cortes and . Vapnik, Support-vector networks, Machine Learning, vol.1, issue.3, pp.273-297, 1995.
DOI : 10.1007/BF00994018

F. Pedregosa, G. Varoquaux, A. Gramfort, V. Michel, B. Thirion et al., Scikit-learn: Machine Learning in Python, Journal of Machine Learning Research, vol.12, pp.2825-2830, 2011.
URL : https://hal.archives-ouvertes.fr/hal-00650905

S. Pakhomov, . Marino, . Se, S. Banks, and C. Bernick, Using automatic speech recognition to assess spoken responses to cognitive tests of semantic verbal fluency, Speech Communication, vol.75, pp.14-26, 2015.
DOI : 10.1016/j.specom.2015.09.010

URL : http://europepmc.org/articles/pmc4662403?pdf=render

M. Lehr, E. Prud-'hommeaux, I. Shafran, and R. , B: Fully automated neuropsychological assessment for detecting Mild Cognitive Impairment, Proceedings of the Annual Conference of the International Speech Communication Association, pp.1039-1042, 2012.

E. Van-den-berg, L. Jiskoot, J. Grosveld, . Van-swieten, . Mc et al., Qualitative Assessment of Verbal Fluency Performance in Frontotemporal Dementia, Dementia and Geriatric Cognitive Disorders, vol.44, issue.1-2, pp.2017-2052, 2017.
DOI : 10.1016/S0028-3932(01)00132-4