Automated Prognosis of Alzheimer’s Disease using Machine Learning Classifiers on Spontaneous Speech features



Alzheimer's disease, Covid-19, Dementia, Machine Learning Algorithm, Neuropsychological Assessment, Support Vector Machine


Early prediction of Alzheimer's disease and related Dementia has been a great challenge. Recently, preliminary research has shown that neurological symptoms in Covid-19 patients may accelerate the onset of Alzheimer's disease. With such a further rise in Alzheimer's and related Dementia cases, having an early prediction system becomes vital. Speech can provide a non-invasive diagnostic marker for such neurodegenerative diseases. This work mainly focuses on studying significant temporal speech features extracted directly from the recordings of the Dementia bank dataset and applying Machine Learning algorithms to classify the Alzheimer's disease related Dementia Group and the healthy control group. The result shows that Support Vector Machine outperformed other machine learning algorithms with an accuracy of 87%. Compared to prior research, which used manual transcriptions provided with the dataset, this study used audio recordings from the Dementia bank dataset and an advanced Automatic Speech Recognizer to extract speech features from the audio recordings. Furthermore, this method can be applied to the spoken responses of subjects during a neuropsychological assessment.


Download data is not yet available.


Alzheimer's disease facts and figures. Alzheimers Dement. 2020 Mar 10. doi: 10.1002/alz.12068. Epub ahead of print. PMID: 32157811.

K. S. Santacruz and D. Swagerty, “Early Diagnosis of Dementia,” Amer. Family Phys., vol. 63, no. 4, pp.703–713, Feb.15, 2001.

Alzheimer’s Association International Conference (AAIC) 2021 – “Covid-19 Associated with Long-term Cognitive Dysfunction, Acceleration of Alzheimer’s Symptoms,” [Online]. Available:

James E. Galvin, “Using Informant and Performance Screening Methods to Detect Mild Cognitive Impairment and Dementia,” Current Geriatrics Reports, Springer Science and Business Media, LLC, part of Springer Nature, vol.7, pp. 19–25, Jan.26, 2018.

M. F. Folstein, S. E. Folstein, and P. R. McHugh, “Mini mental state: A practical method for grading the Cognitive State of patients for the clinician.,” J. Psychiatric Res., vol. 12, no. 3, pp.189-198, 1975.

K. L. de Ipina, J.-B. Alonso, C. M. Travieso, J. Sol-Casals, H. Egi- ˜ raun, M. Faundez-Zanuy, A. Ezeiza, N. Barroso, M. Ecay-Torres, P. Martinez-Lage, and U. M. de Lizardui, “On the selection of non-invasive methods based on Speech Analysis oriented to Automatic Alzheimer disease diagnosis,” Sensors., vol.13, no.5, pp.6730–6745, May 23, 2013.

Ildiko Hoffmann, Dezso Nemeth, Cristina D. Dye, Magdolna Pakaski, Tamas Irinyi, Janos Kalman, “Temporal parameters of Spontaneous Speech in Alzheimer's Disease,” International Journal of Speech-Language Pathology., vol.12, no.1, pp.29–34, Nov. 4, 2010.

B. Roark, M. Mitchell, J.-P. Hosom, K. Hollingshead, and J. Kaye, “Spoken language derived measures for detecting Mild Cognitive Impairment,” IEEE Transactions on Audio, Speech, and Language Processing, vol.19, no.7, pp.2081–2090, Sep. 1, 2011.

C. Laske, H. R. Sohrabi, S. M. Frost, K. L. de-Ipina, P. Garrard, M. Buscema, J. Dauwels, S. R. Soekadar, S. Mueller, C. Linnemann, S. A. Bridenbaugh, Y. Kanagasingam, R. N. Martins, S. E. O’Bryant, “Innovative Diagnostic tools for Early Detection of Alzheimer’s Disease,” Alzheimer’s & Dementia, pp.1-18,2014.

M. Lehr, E.T. Prudhommeaux, I. Shafran, and B. Roark, “Fully Automated Neuropsychological Assessment for detecting Mild Cognitive Impairment,” Proceedings of Interspeech, pp.1039-1042,2012.

W. Jarrold, B. Peintner, D. Wilkins, D. Vergryi, C. Richey, M. L. Gorno-Tempini, and J. Ogar, “Aided Diagnosis of Dementia type through Computer-based Analysis of Spontaneous Speech,” Proceedings of ACL Workshop Computational Linguistics and Clinical Psychology, Baltimore, Maryland, USA, pp. 27– 37, Jun.27,2014.

S. O. Orimaye, J. S.M. Wong, and K. J. Golden, “Learning predictive linguistic features for Alzheimer's Disease and related Dementias using Verbal Utterances,” Workshop on Computational Linguistics and Clinical Psychology: From Linguistic Signal to Clinical Reality, Baltimore, Maryland USA, pp. 78–87, Jun.27,2014.

Ali Khodabakhsh, Serhan Kuscuoglu, Cenk Demiroglu, “Natural Language Features for Detection of Alzheimer’s Disease in Conversational Speech,” IEEE-EMBS International Conference on Biomedical and Health Informatics (BHI), pp. 581-584, 2014.

L. Toth, G. Gosztolya, V. Vincze, I. Hoffmann, G. Szatloczki, E. Biro, F. Zsura, M. Pakaski, J. Kalman, “Automatic Detection of Mild Cognitive Impairment from Spontaneous Speech using ASR,” Proceedings of Interspeech, Dresden Germany, pp. 2694–2698, 2015.

Nikhil Yadav, Christian Poellabauer, Louis Daudet, “Portable Neurological Disease Assessment Using Temporal Analysis of Speech,” ACM BCB’15, Atlanta GA USA, pp. 77-85, Sep. 9-12, 2015.

Alexandra Konig, Aharon Satt, Alexander Sorin, Ron Hoory, Orith Toledo-Ronen, Alexandre Derreumaux, Valeria Manera, Frans Verhey, Pauline Aalten, Phillipe H. Robert, Renaud David, “Automatic Speech Analysis for the Assessment of Patients with predementia and Alzheimer's Disease, Alzheimer’s Dementia, Diagnosis, Assessment and Disease Monitoring, vol. 1, no.1, pp.112-124, 2015.

K. C. Fraser, J. A. Meltzer, F. Rudzicz, “Linguistic features identify Alzheimer's disease in narrative speech,” Journal of Alzheimer's Disease. Vol. 49, no.2, pp.407-422, Aug.20,2015.

E. Aramaki, S. Shikata, M. Miyabe, and A. Kinoshita, “Vocabulary size in speech may be an early indicator of cognitive impairment,” PloS ONE, vol.11, no.5, pp.155-195, 2016.

Gabor Gosztolya, Laszlo Toth, Tamas Grosz, Veronika Vincze, Ildiko Hoffmann, Greta Szatloczki, Magdolna Pakaski, Janos Kalman, “Detecting Mild Cognitive Impairment from Spontaneous Speech by Correlation-Based Phonetic Feature Selection,” INTERSPEECH, San Francisco, USA, pp. 107-111, 2016.

Ane Alberdi Aramendi, Asier Aztiria, Adrian Basarab, “On the early diagnosis of Alzheimer’s Disease from multimodal signals: A survey,” Artificial Intelligence in Medicine, vol. 71, pp.1-29, 2016.

Hiroki Tanaka, Hiroyoshi Adachi, Norimichi Ukita, Takashi Kudo, Satoshi Nakamura, “Automatic Detection of Very Early Stage of Dementia through Multimodal Interaction with Computer Avatars,” ICMI’16, Tokyo, Japan, pp.12–16,2016.

H Tanaka, H Adachi, N Ukita, M Ikeda, H Kazui, T kudo, and S Nakamura, “Detecting Dementia Through Interactive Computer Avatars,” IEEE journal of Translational Engineering in Health and Medicine, vol. 5,2017.

Leandro B. dos Santos, Edilson A. Correa Jr, Osvaldo N. Oliveira Jr, Diego R. Amancio, Letıcia L. Mansur, Sandra M. Aluısio, “Enriching Complex Networks with Word Embeddings for Detecting Mild Cognitive Impairment from Speech Transcripts,” arXiv:1704.08088v1 [cs.CL],2017.

Laszlo Toth, Ildiko Hoffmann, Gabor Gosztolya, Veronika Vincze, Greta Szatlbczki, Zoltan Banreti, Magdolna Pakaski, Janos Kalman, “A Speech recognition-based Solution for the Automatic Detection of Mild Cognitive Impairment from Spontaneous Speech,” Current Alzheimer’s Research, Benthem Science Publisher, pp.130-138,2018.

Becker JT, Boiler F, Lopez OL, Saxton J, McGonigle KL, “The Natural History of Alzheimer’s Disease: Description of study cohort and accuracy of diagnosis,” Arch Neurol. Vol.51, pp.585-594,1994.

Goodglass H, Kaplan E, “The Boston Diagnostic Aphasia Examination,” Lea & Febinger, Philadelphia,1983.

Fabian Pedregosa, Gael Varoquaux, Alexandre Gramfort, Vincent Michel, Bertrand Thirion, Olivier Grisel, Mathieu Blondel, Peter Prettenhofer, Ron Weiss, Vincent Dubourg, Jake Vanderplas, Alexandre Passos, David Cournapeau, Matthieu Brucher, Matthieu Perrot, and Edouard Duchesnay, “Scikit-learn: Machine Learning in Python,” Journal of Machine Learning Research, vol.12, pp.2825–2830,2011.

Gerard Biau, “Analysis of a Random Forests Model,” Journal of Machine Learning Research, vol. 13, pp. 1063-1095, 2012.

J. F. Pitrelli, R. Bakis, E. M. Eide, R. Fernandez, W. Hamza and M. A. Picheny, “The IBM expressive text-to-speech synthesis system for American English,” IEEE Transactions on Audio, Speech, and Language Processing, vol. 14, no. 4, pp. 1099-1108, July 2006.

Saturnino Luz, “Longitudinal Monitoring and Detection of Alzheimer’s Type Dementia from Spontaneous Speech Data,”, IEEE 30th International Symposium on Computer-Based Medical System, 2017.

Roark B, Hosom J-P, Mitchell M, Kaye J, “Automatically derived Spoken Language markers for detecting Mild Cognitive Impairment,” proceedings of the 2nd international conference on technology and aging (ICTA), pp.1–4,2007.

Coulston R, Klabbers E, Villiers J, Hosom J-P, “Application of speech technology in a home-based assessment kiosk for early detection of Alzheimer’s disease,” INTERSPEECH, 8th annual conference of the international speech communication association, Antwerp, Belgium: Aug.27-31,2007.

Block Diagram




How to Cite

S. . Karande and V. . Kulkarni, “Automated Prognosis of Alzheimer’s Disease using Machine Learning Classifiers on Spontaneous Speech features”, Int J Intell Syst Appl Eng, vol. 11, no. 2, pp. 245–251, Feb. 2023.



Research Article