A Spoken Language Database for Research on Moderate Cognitive Impairment: Design and Preliminary Analysis

  • Fernando Espinoza-Cuadros
  • Marlene A. Garcia-Zamora
  • Dania Torres-Boza
  • Carlos A. Ferrer-Riesgo
  • Ana Montero-Benavides
  • Eduardo Gonzalez-Moreira
  • Luis A. Hernandez-Gómez
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8854)


This paper addresses the use of spoken language technologies to identify cognitive impairment through the degree of speech deficits. We present the design of a spoken language database where patients’ voices are collected during regular clinical screening tests for cognitive impairment. Three different speaking styles are recorded: dialogues during structured interviews, readings of a short-passage and verbal picture descriptions. We hope these different spoken materials will help promoting the research on a wide range of spoken language technologies in assessing Moderate Cognitive Impairment (MCI). To illustrate this, a preliminary analysis on the speech recorded from a small group of MCI patients and healthy elder controls is also presented. A Random Forest classifier working on seven prosodic measures extracted from the reading task achieved 78.9% accuracy for MCI detection when compared with a control group, suggesting that these measures can offer a sensitive method of assessing speech output in MCI. This experimental framework shows the potential of the presented spoken language database for the research on automatic and objective identification of early symptoms of MCI in elderly adults.


moderate cognitive impairment spoken language database prosodic analysis 


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  1. 1.
    Llibre, J.J.: Aging and dementia: implications for the scientist community, public health and Cuban society. Rev. Academia de Ciencias de Cuba 2(2), 36–54 (2012)Google Scholar
  2. 2.
    Montenegro Peña, M., Montejo Carrascoa, P., LlaneroLuquea, M., Reinoso García, A.I.: Evaluación y diagnóstico del deterioro cognitivo leve. Revista de Logopedia, Foniatría y Audiología 32, 47–56 (2012)CrossRefGoogle Scholar
  3. 3.
    De Leon, M.J., De Santi, S., Zinkowski, R., Mehta, P.D., Pratico, D., Segal, S., Clark, C., Kerkman, D., De Bernardis, J., Li, J., Lair, L., Reisberg, B., Tsui, W., Rusinek, H.: MRI and CSF studies in the early diagnosis of Alzheimer’s disease. Journal of Internal Medicine 256, 205–223 (2004)CrossRefGoogle Scholar
  4. 4.
    De Leon, M.J., Mosconi, L., Blennow, K., DeSanti, S., Zinkowski, R., Mehta, P.D., Rusinek, H.: Imaging and CSF studies in the preclinical diagnosis of Alzheimer’s disease. Annals of the New York Academy of Sciences 1097(1), 114–145 (2007)CrossRefGoogle Scholar
  5. 5.
    Folstein, M.F., Folstein, S.E., McHugh, P.R.: Mini-mental state. A practical method for grading the cognitive state of patients for the clinician. J. Psychiatr. Res. 12(3), 189–198 (1975)CrossRefGoogle Scholar
  6. 6.
    Morris, J.C.: The Clinical Dementia Rating (CDR): current version and scoring rules. J. Neurology 43, 2412–2414 (1993)CrossRefGoogle Scholar
  7. 7.
    Buschke, H., Kuslansky, G., Katz, M., Stewart, W.F., Sliwinski, M.J., Eckholdt, H.M., Lipton, R.B.: Screening for dementia with the Memory Impairment Screen. Neurology 52(2), 231–238 (1999)CrossRefGoogle Scholar
  8. 8.
    Deramecourt, D., Lebert, F., Debachy, B., Mackowiak-Cordoliani, M.A., Bombois, S., Kerdraon, O., et al.: Prediction of pathology in primary progressive language and speech disorders. Neurology 74, 42–49 (2010)CrossRefGoogle Scholar
  9. 9.
    Mesulam, M., Wicklund, A., Johnson, N., Rogalski, E., Léger, G.C., Ra-demaker, A., et al.: Alzheimer and frontotemporal pathology in subsets of primary progressive aphasia. Annual Neurology 63, 709–719 (2008)CrossRefGoogle Scholar
  10. 10.
    Lehr, M., Prud’hommeaux, E., Shafran, I., Roark, B.: Fully automated neuropsychological assessment for detecting mild cognitive impairment. In: Interspeech 2012 (2012)Google Scholar
  11. 11.
    Hakkani-Tür, D., Vergyri, D., Tür, G.: Speech-based automated cognitive status assessment. In: INTERSPEECH, pp. 258–261 (2010)Google Scholar
  12. 12.
    Kato, S., Endo, H., Homma, A., Sakuma, T., Watanabe, K.: Early detection of cognitive impairment in the elderly based on Bayesian mining using speech prosody and cerebral blood flow activation. In: 2013 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 5813–5816 (2013)Google Scholar
  13. 13.
    Thomas, C., Keselj, V., Cercone, N., Rockwood, K., Asp, E.: Automatic detection and rating of dementia of Alzheimer type through lexical analysis of spontaneous speech. In: 2005 IEEE International Conference on Mechatronics and Automation, vol. 3, pp. 1569–1574 (2005)Google Scholar
  14. 14.
    Bucks, R.S., Singh, S., Cuerden, J.M., Wilcock, G.K.: Analysis of spontaneous, conversational speech in dementia of Alzheimer type: Evaluation of an objective technique for analysing lexical performance. Aphasiology 14, 71–91 (2000)CrossRefGoogle Scholar
  15. 15.
    Rochford, I., Rapcan, V., D’Arcy, S., Reilly, R.B.: Dynamic minimum pause threshold estimation for speech analysis in studies of cognitive function in ageing. In: 2012 Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3700–3703 (2012)Google Scholar
  16. 16.
    Martínez-Sánchez, F., Meilán, J.J.G., García-Sevilla, J., Carro, J., Arana, J.M.: Análisis de la fluencialectora en pacientes con la enfermedad de Alzheimer y controles asintomáticos. Neurología 28(6), 325–331 (2013)CrossRefGoogle Scholar
  17. 17.
    Darley, F.L., Aronson, A.E., Brown, J.R.: Motor speech disorders, 3rd edn. W.B. Saunders Company, Philadelphia (1975)Google Scholar
  18. 18.
    Risser, A.H., Spreen, O.: The western aphasia battery. Journal of Clinical and Experimental Neuropsychology 7(4), 463–470 (1985)CrossRefGoogle Scholar
  19. 19.
    Mertens, P.: Automatic segmentation of speech into syllables. In: Laver, J., Jack, M. (eds.) Proceedings of the European Conference on Speech Technology, Edinburgh, vol. 2, pp. 9–12 (1987)Google Scholar
  20. 20.
    Massey Jr., F.J.: The Kolmogorov-Smirnov test for goodness of fit. Journal of the American Statistical Association 46(253), 68–78 (1951)CrossRefzbMATHGoogle Scholar
  21. 21.
    Breiman, L.: Random Forest. Machine Learning 45, 5–32 (2001)CrossRefzbMATHGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Fernando Espinoza-Cuadros
    • 1
  • Marlene A. Garcia-Zamora
    • 2
  • Dania Torres-Boza
    • 3
  • Carlos A. Ferrer-Riesgo
    • 3
  • Ana Montero-Benavides
    • 1
  • Eduardo Gonzalez-Moreira
    • 3
  • Luis A. Hernandez-Gómez
    • 1
  1. 1.Departamento de Señales, Sistemas y RadiocomunicacionesUniversidad Politécnica de MadridMadridSpain
  2. 2.Center for Elderly Adults #2Santa ClaraCuba
  3. 3.Center for Studies on Electronics and Information TechnologiesUniversidad Central “Marta Abreu” de Las VillasSanta ClaraCuba

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