Identifying Alzheimer's disease through speech: a multilingual approach
DOI:
https://doi.org/10.59681/2175-4411.v16.iEspecial.2024.1273Keywords:
Machine Learning, Automatic Speech Analysis, Alzheimer's DiseaseAbstract
Alzheimer's disease, the leading form of dementia among elderly individuals worldwide, has significant social and economic repercussions. It is characterized by memory loss and changes in language, cognition, and emotions, irreversibly affecting neurons. Early diagnosis is crucial but challenging, as it relies on detailed medical evaluations, cognitive tests, and complex exams that are often expensive and inaccessible, particularly for low-income individuals. In this context, advanced computational techniques, such as machine learning (ML), emerge as promising non-invasive alternatives for the early detection of the disease. This study introduces a multilingual ML-based approach focusing on paralinguistic and emotional speech characteristics as biomarkers for Alzheimer's identification. The experiments yielded results with accuracies reaching 81% for English and 87.50% for Portuguese. Additionally, integrating this methodology with the state-of-the-art model by Haider, Fuente, and Luz(1) resulted in an average accuracy of 81.70%, surpassing their original results.
References
Haider F, de la Fuente S, Luz S. An Assessment of Paralinguistic Acoustic Features for Detection of Alzheimer’s Dementia in Spontaneous Speech. IEEE Journal of Selected Topics in Signal Processing. 2020 Feb;14:272-281. DOI: https://doi.org/10.1109/JSTSP.2019.2955022
World Health Organization. Ageing. World Health Organization (WHO) [Internet]. 2024 [cited 2024 Jan 15]; [about 1 p.]. Available from: https://www.who.int/health-topics/ageing.
Long S, Benoist C, Weidner W. World Alzheimer Report 2023: Reducing dementia risk: never too early, never too late. London, England: Alzheimer’s Disease International (ADI); 2023.
Campbell EL, Mesía RY, Docío-Fernández L, García-Mateo C. Paralinguistic and linguistic fluency features for Alzheimer’s disease detection. Computer Speech & Language. 2021. Jul;68:101198. DOI: https://doi.org/10.1016/j.csl.2021.101198
de la Fuente Garcia S, Haider F, Luz S. Cross-corpus Feature Learning between Spontaneous Monologue and Dialogue for Automatic Classification of Alzheimer’s Dementia Speech. 2020 42nd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC). 2020 Jul 20-24; Montreal, Canada. p. 5851-5855. DOI: https://doi.org/10.1109/EMBC44109.2020.9176305
Bernieri G, Duarte JC. Identifying Alzheimer’s Disease Through Speech Using Emotion Recognition. Journal of Health Informatics. 2023 Jul 20;15:1-14. DOI: https://doi.org/10.59681/2175-4411.v15.iEspecial.2023.1093
Russell JA, Mehrabian A. Evidence for a three-factor theory of emotions. Journal of Research in Personality. 1977 Sep;11:273–294. DOI: https://doi.org/10.1016/0092-6566(77)90037-X
Cai H, Huang X, Liu Z, Liao W, Dai H, Wu Z, et al. Multimodal Approaches for Alzheimer’s Detection Using Patients’ Speech and Transcript. Lecture Notes in Computer Science. 2023 Sep 13;395–406. DOI: https://doi.org/10.1007/978-3-031-43075-6_34
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. Archives of Neurology. 1994 Jun; 51 (6): 585-594. DOI: https://doi.org/10.1001/archneur.1994.00540180063015
Burkhardt F, Paeschke A, Rolfes M, Sendlmeier WF, Weiss B. A database of German emotional speech. In: Proceedings of Interspeech 2005; 2005 Sep 04-08; Lisbon, Portugal. p. 1517-1520. DOI: https://doi.org/10.21437/Interspeech.2005-446
Aluísio S, Cunha A, Scarton C. Evaluating Progression of Alzheimer’s Disease by Regression and Classification Methods in a Narrative Language Test in Portuguese. Computational Processing of the Portuguese Language. 2016;109–114. DOI: https://doi.org/10.1007/978-3-319-41552-9_10
Husein Z. Malaya, Speech-Toolkit library. Version 1.2.7 [software]. 2020 [cited 2024 Jan 15]. Available from: https://github.com/huseinzol05/malaya-speech.
Buechel S, Hahn U. Emotion Analysis as a Regression Problem – Dimensional Models and Their Implications on Emotion Representation and Metrical Evaluation. European Conference on Artificial Intelligence 2016; 2016. p. 1114-1122.
Wagner J, Triantafyllopoulos A, Wierstorf H, Schmitt M, Burkhardt F, Eyben F, et al. Dawn of the transformer era in speech emotion recognition: Closing the valence gap. IEEE Transactions on Pattern Analysis and Machine Intelligence. 2022; 1–13.
Shreffler J, Huecker MR. Diagnostic Testing Accuracy: Sensitivity, Specificity, Predictive Values and Likelihood Ratios. StatPearls. 2023.
Downloads
Published
How to Cite
Issue
Section
License
This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
Submission of a paper to Journal of Health Informatics is understood to imply that it is not being considered for publication elsewhere and that the author(s) permission to publish his/her (their) article(s) in this Journal implies the exclusive authorization of the publishers to deal with all issues concerning the copyright therein. Upon the submission of an article, authors will be asked to sign a Copyright Notice. Acceptance of the agreement will ensure the widest possible dissemination of information. An e-mail will be sent to the corresponding author confirming receipt of the manuscript and acceptance of the agreement.
