Skip to main content

On Automatic Diagnosis of Alzheimer’s Disease Based on Spontaneous Speech Analysis and Emotional Temperature


Alzheimer’s disease (AD) is the most prevalent form of progressive degenerative dementia; it has a high socioeconomic impact in Western countries. Therefore, it is one of the most active research areas today. Alzheimer’s disease is sometimes diagnosed by excluding other dementias, and definitive confirmation is only obtained through a postmortem study of the brain tissue of the patient. The work presented here is part of a larger study that aims to identify novel technologies and biomarkers for early AD detection, and it focuses on evaluating the suitability of a new approach for early diagnosis of AD by noninvasive methods. The purpose is to examine, in a pilot study, the potential of applying machine learning algorithms to speech features obtained from suspected Alzheimer’s disease sufferers in order to help diagnose this disease and determine its degree of severity. Two human capabilities relevant in communication have been analyzed for feature selection: spontaneous speech and emotional response. The experimental results obtained were very satisfactory and promising for the early diagnosis and classification of AD patients.

This is a preview of subscription content, access via your institution.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10


  1. McKahn G, Drachman D, Folstein M, Katzman R, Price D, Stadlan EM. Clinical diagnosis of Alzheimer’s disease: report of the NINCDS-ADRDA workgroup on Alzheimer’s disease. Neurology. 1984;24:939–44.

    Article  Google Scholar 

  2. McKhann GM, Knopman DS, Chertkow H, Hyman BT, Jack CR Jr, Kawas CH, Klunk WE, Koroshetz WJ, Manly JJ, Mayeux R, Mohs RC, Morris JC, Rossor MN, Scheltens P, Carrillo MC, Thies B, Weintraub S, Phelps CH. The diagnosis of dementia due to Alzheimer’s disease: recommendations from the National Institute on Aging-Alzheimer’s Association workgroups on diagnostic guidelines for Alzheimer’s disease (PDF). Alzheimers Dement. 2011;7(3):263–9.

    Article  PubMed Central  PubMed  Google Scholar 

  3. Pole Van de LA, Flier Van der WM, Hensel A, Gertz HJ, Scheltens P. The effects of age and Alzheimer’s disease on hippocampal volumes, a MRI study. Alzheimers Dement. 2005;1(Supplement 1):51. doi:10.1016/j.jalz.2005.06.205.

    Article  Google Scholar 

  4. Morris JC. The clinical dementia rating (CDR): current version and scoring rules. Neurology. 1993;43(11):2412b–4b.

    Article  Google Scholar 

  5. Diagnostic and Statistical Manual of Mental disorders. Text Revision: DSM-IV-TR. Washington DC, USA: American Psychiatric Association; 2000.

    Google Scholar 

  6. Petrella JR, Coleman R, Doraiswamy P. Neuroimaging and early diagnosis of Alzheimer’s disease: a look to the future. Radiology. 2003;226:315–36.

    Article  PubMed  Google Scholar 

  7. Wernickand MN, Aarsvold JN. EmissionTomography: TheFundamentals of PET and SPECT. Elsevier New York: Publisher; 2004.

    Google Scholar 

  8. Pareto D, Aguiar P, Pavia J, Gispert J, Cot A, Falcon C, Benabarre A, Lomena F, Vieta E, Ros D. Assessment of SPM in per-fusion brain SPECT studies. A numerical simulation study using boot-strap resampling methods. EEE Trans Biomed Eng. 2008;55(7):1849–53.

    Article  Google Scholar 

  9. Álvarez I, Górriz JM, Ramírez J, Salas-Gonzalez D, López M, Segovia F, Padilla P, Gracía C. Projecting independent components of SPECT images for computer aided diagnosis of Alzheimer’s disease. Pattern Recogn Lett. 2010;31(11):1342–7.

    Article  Google Scholar 

  10. Alzheimer’s Association. Available online:

  11. Faundez-Zanuy M, Hussain A, Mekyska J, Sesa-Nogueras E, Monte-Moreno E, Esposito A, Chetouani M, Garre-Olmo J, Abel A, Smekal Z, Lopez-de-Ipiña K. Biometric applications related to human beings: there is life beyond security. Cognit Comput. 2012;5(1):136–51. doi:10.1007/s12559-012-9169-9.

    Article  Google Scholar 

  12. Sesa-Nogueras E, Faundez-Zanuy M, Mekyska J. An information analysis of in-air and on-surface trajectories in online handwriting. Cognit Comput. 2013;4(1):195–205. doi:10.1007/s12559-011-9119-y.

    Google Scholar 

  13. Gómez-Vilda P, Rodellar-Biarge V, Nieto-Lluis V, Muñoz-Mulas C, Mazaira-Fernández LM, Martínez-Olalla R, Álvarez-Marquina A, Ramírez-Calvo C, Fernández-Fernández M. Characterizing neurological disease from voice quality biomechanical analysis. Cognit Comput. 2013;. doi:10.1007/s12559-013-9207-2.

    Google Scholar 

  14. Henríquez P, Alonso-Hernández JB, Ferrer-Ballester MA, Travieso-González CM, Orozco-Arroyave JR. Global selection of features for nonlinear dynamics characterization of emotional speech. Cognit Comput. 2012;. doi:10.1007/s12559-013-9157-0.

    Google Scholar 

  15. López de Ipiña K, Alonso JB, Solé-Casals J, Barroso N, Faundez M, Ecay M, Travieso C, Ezeiza A, Estanga A. Alzheimer’s disease diagnosis based on automatic spontaneous speech analysis. IWAAL special session in challenges in Neuroengineering. In: Proceedings of International Conference on Neural Computation Theory and Applications (NCTA). Barcelona; 2012.

  16. Buiza C. Evaluación y tratamiento de los trastornos del lenguaje. Donostia: Matia Fundazioa; 2010.

    Google Scholar 

  17. Martinez F, Garcia J, Perez E, Carro J, Anara JM. Patrones de Prosodia expresiva en pacientes con enfermedad de Alzheimer. Psicothema. 2012;24(1):16–21.

    Google Scholar 

  18. Hu WT, McMillan C, Libon D, Leight S, Forman M, Lee VMY, Trojanowski JQ, Grossman M. Multimodal predictors for Alzheimer’s disease in non fluent primary progressive aphasia. Neurology. 2010;75(7):595–602.

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  19. Shimokawa A, Yatomi N, Anamizu S, Torii S, Isono H, Sugai Y, Kohno M. Influence of deteriorating ability of emotional comprehension on interpersonal behaviour in Alzheimer-type dementia. Brain Cogn. 2001;47:423–33.

    Article  CAS  PubMed  Google Scholar 

  20. Goodkind MS, Gyurak A, McCarthy M, Miller BL, Levenson RW. Emotion regulation deficits in frontotemporal lobar degeneration and Alzheimer’s disease. Psychol Aging. 2010;25(1):30–7. doi:10.1037/a0018519.

    Article  PubMed Central  PubMed  Google Scholar 

  21. Cadieux N, Greeve K. Emotion processing in Alzheimer’s disease. J Int Neuropsychol Soc. 1997;3:411–9.

    CAS  PubMed  Google Scholar 

  22. Horley K, Reid A, Burnham D. Emotional prosody perception and production in dementia of the Alzheimer’s type. J Speech Lang Hear Res. 2010;53(5):1132–46. doi:10.1044/1092-4388(2010/09-0030.

    Article  PubMed  Google Scholar 

  23. Henry JD, Rendell PG, Scicluna A, Jackson M, Phillips LH. Emotion experience, expression, and regulation in Alzheimer’s disease. Psychol Aging. 2009;24(1):252–7.

    Article  PubMed  Google Scholar 

  24. Knapp ML. Essentials of nonverbal communication. NY: Holt, Rinehart & Winston; 1980.

    Google Scholar 

  25. Cowie R, Douglas-Cowie E, Tsapatsoulis N, Votsis G, Kollias S, Fellenz W, Taylor JG. Emotion recognition in human-computer interaction. IEEE Signal Process Mag. 2001;18(1):32–80.

    Article  Google Scholar 

  26. Plutchnik R. Emotion: A psychoevolutionary synthesis. USA: Harper and Row; 1980.

    Google Scholar 

  27. Praat: doing phonetics by computer. Available online:

  28. Voice activity detector algorithm (VAD). Available online:

  29. Solé J, Zaiats V. A non-linear VAD for noisy environment. Cognit Comput. 2010;2(3):191–8.

    Article  Google Scholar 

  30. Rahman MM, Bhuiyan MA. Continuous bangla speech segmentation using short-term speech features extraction approaches. Int J Adv Computer Sci Appl. 2012;3–11:131–8.

    Google Scholar 

  31. Pao TL, Chien CS, Yen JH, Chen YT, Cheng YM. Continuous tracking of user emotion in mandarin emotional speech. In: Proceedings of 3rd International Conference on International Information Hiding and Multimedia Signal Processing (IIH-MSP’07), Splendor Kaohsiung, Taiwan; 2007 November 26–28; 1:47–52.

  32. Petrushin VA. Emotion in speech: recognition and application to call centers. In: Proceedings, Conference on Artificial Neural Networks in Engineering (ANNIE’99), St. Louis, Missouri, USA; 1999 November 7–10; 7–10.

  33. Lee CM, Narayanan S. Emotion recognition using a data-driven fuzzy interference system. In: Proceedings of 8th European Conference on Speech Communication and Technology (ECSCT’03), Geneva, Switzerland; 2003 September 1–4; 157–160.

  34. Kwon OW, Chan K, Hao J, Lee TW. Emotion recognition by speech signals. In: Proceedings of 8th European Conference on Speech Communication and Technology (ECSCT’03), Geneva, Switzerland; 2003 September 1–4; 125–128.

  35. De Cheveigné A, Kawahara H. YIN, a fundamental frequency estimator for speech and music. J Acoust Soc Am. 2002;111(4):1917–30.

    Article  PubMed  Google Scholar 

  36. Alonso J, De León J, Alonso I, Ferrer MA. Automatic detection of pathologies in the voice by HOS base parameters. J Appl Signal Process. 2001;4:275–84.

    Article  Google Scholar 

  37. Chang CC, Lin CJ. LIBSVM: a library for support vector machines; 2001. Available online:

  38. WEKA. Available online:

  39. Picard R, Cook D. Cross-validation of regression models. J Am Stat Assoc. 1984;79(387):575–83.

    Article  Google Scholar 

Download references


This work has been partially supported by a SAIOTEK grant from the Basque Government, the University of Vic under the research Grant R0904, and the Spanish Ministerio de Ciencia e Innovación TEC2012-38630-C04-03. Professor Iciar Martinez (Research Center for Experimental Marine Biology and Biotechnology-Plentziako Itsas Estazioa (PIE), University of the Basque Country and IKERBASQUE, Basque Foundation for Science) is gratefully acknowledged for helpful discussions and for her contribution to the preparation of the manuscript.

Author information

Authors and Affiliations


Corresponding author

Correspondence to K. López-de-Ipiña.

Rights and permissions

Reprints and Permissions

About this article

Cite this article

López-de-Ipiña, K., Alonso, J.B., Solé-Casals, J. et al. On Automatic Diagnosis of Alzheimer’s Disease Based on Spontaneous Speech Analysis and Emotional Temperature. Cogn Comput 7, 44–55 (2015).

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: