ML-Based Quantitative Analysis of Linguistic and Speech Features Relevant in Predicting Alzheimer’s Disease
Abstract Alzheimer’s disease (AD) is a severe neurological condition that affects numerous people globally with detrimental consequences. Detecting AD early is crucial for prompt treatment and effective management. This study presents a novel approach for detecting and classifying six types of cognitive impairment using speech-based analysis, including probable AD, possible AD, mild cognitive impairment (MCI), memory impairments, vascular dementia, and control. The method employs speech data from DementiaBank’s Pitt Corpus, which is preprocessed and analyzed to extract pertinent acoustic features. The characteristics are subsequently used to educate five machine learning algorithms, namely k-nearest neighbors (KNN), decision tree (DT), support vector machine (SVM), XGBoost, and random forest (RF). The effectiveness of every algorithm is assessed through a 10-fold cross-validation. According to the research findings, the suggested method based on speech obtains a total accuracy of 75.59% concerning the six-class categorization issue. Among the five machine learning algorithms tested, the XGBoost classifier showed the highest accuracy of 75.59%. These findings indicate that speech-based approaches can potentially be valuable for detecting and classifying cognitive impairment, including AD. The paper also explores robustness testing, evaluating the algorithms’ performance under various circumstances, such as noise variability, voice quality changes, and accent variations. The proposed approach can be developed into a noninvasive, cost-effective, and accessible diagnostic tool for the early detection and management of cognitive impairment.
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AI-Atroshi, C., Rene Beulah, J., Singamaneni, K. K., Pretty Diana Cyril, C., Neelakandan, S., & Velmurugan, S. (2022). Automated speech-based evaluation of mild cognitive impairment and Alzheimer’s disease detection using a deep belief network model. International Journal of Healthcare Management, 1-11. https://doi.org/10.1080/20479700.2022.2097764
Bertini, F., Allevi, D., Lutero, G., Calzà, L., & Montesi, D. (2022). An automatic Alzheimer’s disease classifier based on spontaneous spoken English. Computer Speech & Language, 72, 101298. https://doi.org/10.1016/j.csl.2021.101298
Casanova, E., Treviso, M., Hübner, L., & Aluísio, S. (2020). I am evaluating Sentence Segmentation in Different Datasets of Neuropsychological Language Tests in Brazilian Portuguese. Proceedings of the Twelfth Language Resources and Evaluation Conference, 2605-2614. Paris: European Language Resources Association.
Chen, J., Zhu, J., & Ye, J. (2019). An Attention-Based Hybrid Network for Automatic Detection of Alzhei-mer’s Disease from Narrative Speech. Interspeech, 4085-4089. https://doi.org/10.21437/Interspeech.2019-2872
Chien, Y. W., Hong, S. Y., Cheah, W. T., Yao, L. H., Chang, Y. L., & Fu, L. C. (2019). An automatic assessment system for Alzheimer’s disease based on speech using a feature sequence generator and recurrent neural network. Scientific Reports, 9(1), 1-10. https://doi.org/10.1038/s41598-019-56020-x
Chlasta, K., & Wołk, K. (2021). Towards computer-based automated screening of dementia through spontaneous speech. Frontiers in Psychology, 11, 623237. https://doi.org/10.3389/fpsyg.2020.623237
Cummins, N., Pan, Y., Ren, Z., Fritsch, J., Nallanthighal, V. S., Christensen, H., … & Härmä, A. (2020). A comparison of acoustic and linguistics methodologies for Alzheimer’s dementia recognition. Interspeech 2020, 2182-2186. ISCA-International Speech Communication Association. https://doi.org/10.21437/Interspeech.2020-2635
Fritsch, J., Wankerl, S., & Nöth, E. (2019). Automatic diagnosis of Alzheimer’s disease using neural network lan-guage models. ICASSP 2019-2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 5841-5845. IEEE. https://doi.org/10.1109/ICASSP.2019.8682690
Gauder, L., Pepino, L., Ferrer, L., & Riera, P. (2021). Alzheimer Disease Recognition Using Speech-Based Embeddings from Pre-Trained Models. Interspeech, 3795-3799. https://doi.org/10.21437/Interspeech.2021-753
Hong, S. Y., Yao, L. H., Cheah, W. T., Chang, W. D., Fu, L. C., & Chang, Y. L. (2019). A novel screening sys-tem for Alzheimer’s disease based on speech transcripts using neural network. 2019 IEEE International Conference on Systems, Man and Cybernetics (SMC), 2440-2445. IEEE. https://doi.org/10.1109/SMC.2019.8914628
Haulcy, R. M., & Glass, J. (2021). Classifying Alzheimer’s disease using audio and text-based representations of speech. Frontiers in Psychology, 11, 624137. https://doi.org/10.3389/fpsyg.2020.624137
Jarrold, W., Peintner, B., Wilkins, D., Vergryi, D., Richey, C., Gorno-Tempini, M. L., & Ogar, J. (2014). Aided diagnosis of dementia type through computer-based analysis of spontaneous speech. Proceedings of the Work-shop on Computational Linguistics and Clinical Psychology: From Linguistic Signal to Clinical Reality, 27-37. https://doi.org/10.3115/v1/W14-3204
Javeed, A., Dallora, A. L., Berglund, J. S., Ali, A., Ali, L., & Anderberg, P. (2023). Machine Learning for Dementia Prediction: A Systematic Review and Future Research Directions. Journal of medical systems, 47(1), 1-25. https://doi.org/10.1007/s10916-023-01906-7
Karlekar, S., Niu, T., & Bansal, M. (2018). Detecting linguistic characteristics of Alzheimer’s dementia by interpreting neural models. arXiv preprint arXiv:1804.06440. https://doi.org/10.18653/v1/N18-2110
Khodabakhsh, A., Kuşxuoğlu, S., & Demiroğlu, C. (2014). Natural language features for detection of Alzheimer’s disease in conversational speech. IEEE-EMBS International Conference on Biomedical and Health Informatics (BHI), 581-584. IEEE. https://doi.org/10.1109/BHI.2014.6864431
Köning, A., Satt, A., Sorin, A., Hoory, R., Toledo-Ronen, O., Derreumaux, A., Manera, V., Verhey, F., Aalten, P., Robert, P.H. and David, R. (2015). Automatic speech analysis for the assessment of patients with predementia and Alzheimer’s disease. Alzheimer’s & Dementia: Diagnosis, Assessment & Disease Monitoring, 1(1), 112-124. https://doi.org/10.1016/j.dadm.2014.11.012
Koo, J., Lee, J. H., Pyo, J., Jo, Y., & Lee, K. (2020). Exploiting multi-modal features from pre-trained networks for Alz-heimer’s dementia recognition. arXiv preprint arXiv:2009.04070. https://doi.org/10.21437/Interspeech.2020-3153
Kumar, M. R., Vekkot, S., Lalitha, S., Gupta, D., Govindraj, V. J., Shaukat, K., … & Zakariah, M. (2022). Dementia De-tection from Speech Using Machine Learning and Deep Learning Architectures. Sensors, 22(23), 9311. https://doi.org/10.3390/s22239311
Kundaram, S. S., & Pathak, K. C. (2021). Deep learning-based Alzheimer’s disease detection. Proceedings of the Fourth International Conference on Microelectronics, Computing, and Communication Systems: MCCS 2019, 587-597. Springer Singapore. https://doi.org/10.1007/978-981-15-5546-6_50
Liu, L., Zhao, S., Chen, H., & Wang, A. (2020). A new machine learning method for identifying Alzheimer’s disease. Simulation Modelling Practice and Theory, 99, 102023. https://doi.org/10.1016/j.simpat.2019.102023
Liu, Z., Guo, Z., Ling, Z., & Li, Y. (2021). Detecting Alzheimer’s disease from speech using neural networks with bottleneck features and data augmentation. ICASSP 2021-2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 7323-7327. IEEE. https://doi.org/10.1109/ICASSP39728.2021.9413566
Meghanani, A., Anoop, C. S., & Ramakrishnan, A. G. (2021). An exploration of log-mel spectrogram and MFCC features for Alzheimer’s dementia recognition from spontaneous speech. 2021 IEEE Spoken Language Technology Workshop (SLT), 670-677. IEEE. https://doi.org/10.1109/SLT48900.2021.9383491
Mittal, A., Sahoo, S., Datar, A., Kadiwala, J., Shalu, H., & Mathew, J. (2020). Multi-modal detection of Alzheimer’s dis-ease from speech and text. arXiv preprint arXiv:2012.00096.
Orimaye, S. O., Wong, J. S. M., & Wong, C. P. (2018). Deep language space neural network for classifying mild cogni-tive impairment and Alzheimer-type dementia. PloS one, 13(11), e0205636. https://doi.org/10.1371/journal.pone.0205636
Pan, Y., Mirheidari, B., Reuber, M., Venneri, A., Blackburn, D., & Christensen, H. (2020). Improving detection of Alzheimer’s Disease using automatic speech recognition to identify high-quality segments for more robust feature extraction. Proceedings of Interspeech 2020, 4961-4965. International Speech Communication Association (ISCA). https://doi.org/10.21437/Interspeech.2020-2698
Pan, Y., Mirheidari, B., Harris, J. M., Thompson, J. C., Jones, M., Snowden, J. S., … & Christensen, H. (2021). Using the Outputs of Different Automatic Speech Recognition Paradigms for Acoustic-and BERT-Based Alzheimer’s Dementia Detection Through Spontaneous Speech. Interspeech, 3810-3814. https://doi.org/10.21437/Interspeech.2021-1519
Pappagari, R., Cho, J., Joshi, S., Moro-Velázquez, L., Zelasko, P., Villalba, J., & Dehak, N. (2021). Automatic Detection and Assessment of Alzheimer’s Disease Using Speech and Language Technologies in Low-Resource Scenarios. Interspeech, 3825-3829. https://doi.org/10.21437/Interspeech.2021-1850
Searle, T., Ibrahim, Z., & Dobson, R. (2020). Comparing natural language processing techniques for Alzheimer’s dementia prediction in spontaneous speech. arXiv preprint arXiv:2006.07358. https://doi.org/10.21437/Interspeech.2020-2729
Tóth, L., Hoffmann, I., Gosztolya, G., Vincze, V., Szatlóczki, G., Bánréti, Z., … & Kálmán, J. (2018). A speech recogni-tion-based solution for automatically detecting mild cognitive impairment from spontaneous speech. Current Alzheimer Research, 15(2), 130-138. https://doi.org/10.2174/1567205014666171121114930
Wang, N., Cao, Y., Hao, S., Shao, Z., & Subbalakshmi, K. P. (2021). Modular Multi-Modal Attention Network for Alzheimer’s Disease Detection Using Patient Audio and Language Data. Interspeech, 3835-3839. https://doi.org/10.21437/Interspeech.2021-2024
Warnita, T., Inoue, N., & Shinoda, K. (2018). Detecting Alzheimer’s disease using gated convolutional neural network from audio data. arXiv preprint arXiv:1803.11344. https://doi.org/10.21437/Interspeech.2018-1713
Xue, C., Karjadi, C., Paschalidis, I. C., Au, R., & Kolachalama, V. B. (2021). Detection of dementia on voice recordings using deep learning: a Framingham Heart Study. Alzheimer’s Research & Therapy, 13, 1-15. https://doi.org/10.1186/s13195-021-00888-3
Zargarbashi, S., & Babaali, B. (2019). A multi-modal feature embedding approach to diagnose Alzheimer’s disease from spoken language. arXiv preprint arXiv:1910.00330.
Bertini, F., Allevi, D., Lutero, G., Calzà, L., & Montesi, D. (2022). An automatic Alzheimer’s disease classifier based on spontaneous spoken English. Computer Speech & Language, 72, 101298. https://doi.org/10.1016/j.csl.2021.101298
Casanova, E., Treviso, M., Hübner, L., & Aluísio, S. (2020). I am evaluating Sentence Segmentation in Different Datasets of Neuropsychological Language Tests in Brazilian Portuguese. Proceedings of the Twelfth Language Resources and Evaluation Conference, 2605-2614. Paris: European Language Resources Association.
Chen, J., Zhu, J., & Ye, J. (2019). An Attention-Based Hybrid Network for Automatic Detection of Alzhei-mer’s Disease from Narrative Speech. Interspeech, 4085-4089. https://doi.org/10.21437/Interspeech.2019-2872
Chien, Y. W., Hong, S. Y., Cheah, W. T., Yao, L. H., Chang, Y. L., & Fu, L. C. (2019). An automatic assessment system for Alzheimer’s disease based on speech using a feature sequence generator and recurrent neural network. Scientific Reports, 9(1), 1-10. https://doi.org/10.1038/s41598-019-56020-x
Chlasta, K., & Wołk, K. (2021). Towards computer-based automated screening of dementia through spontaneous speech. Frontiers in Psychology, 11, 623237. https://doi.org/10.3389/fpsyg.2020.623237
Cummins, N., Pan, Y., Ren, Z., Fritsch, J., Nallanthighal, V. S., Christensen, H., … & Härmä, A. (2020). A comparison of acoustic and linguistics methodologies for Alzheimer’s dementia recognition. Interspeech 2020, 2182-2186. ISCA-International Speech Communication Association. https://doi.org/10.21437/Interspeech.2020-2635
Fritsch, J., Wankerl, S., & Nöth, E. (2019). Automatic diagnosis of Alzheimer’s disease using neural network lan-guage models. ICASSP 2019-2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 5841-5845. IEEE. https://doi.org/10.1109/ICASSP.2019.8682690
Gauder, L., Pepino, L., Ferrer, L., & Riera, P. (2021). Alzheimer Disease Recognition Using Speech-Based Embeddings from Pre-Trained Models. Interspeech, 3795-3799. https://doi.org/10.21437/Interspeech.2021-753
Hong, S. Y., Yao, L. H., Cheah, W. T., Chang, W. D., Fu, L. C., & Chang, Y. L. (2019). A novel screening sys-tem for Alzheimer’s disease based on speech transcripts using neural network. 2019 IEEE International Conference on Systems, Man and Cybernetics (SMC), 2440-2445. IEEE. https://doi.org/10.1109/SMC.2019.8914628
Haulcy, R. M., & Glass, J. (2021). Classifying Alzheimer’s disease using audio and text-based representations of speech. Frontiers in Psychology, 11, 624137. https://doi.org/10.3389/fpsyg.2020.624137
Jarrold, W., Peintner, B., Wilkins, D., Vergryi, D., Richey, C., Gorno-Tempini, M. L., & Ogar, J. (2014). Aided diagnosis of dementia type through computer-based analysis of spontaneous speech. Proceedings of the Work-shop on Computational Linguistics and Clinical Psychology: From Linguistic Signal to Clinical Reality, 27-37. https://doi.org/10.3115/v1/W14-3204
Javeed, A., Dallora, A. L., Berglund, J. S., Ali, A., Ali, L., & Anderberg, P. (2023). Machine Learning for Dementia Prediction: A Systematic Review and Future Research Directions. Journal of medical systems, 47(1), 1-25. https://doi.org/10.1007/s10916-023-01906-7
Karlekar, S., Niu, T., & Bansal, M. (2018). Detecting linguistic characteristics of Alzheimer’s dementia by interpreting neural models. arXiv preprint arXiv:1804.06440. https://doi.org/10.18653/v1/N18-2110
Khodabakhsh, A., Kuşxuoğlu, S., & Demiroğlu, C. (2014). Natural language features for detection of Alzheimer’s disease in conversational speech. IEEE-EMBS International Conference on Biomedical and Health Informatics (BHI), 581-584. IEEE. https://doi.org/10.1109/BHI.2014.6864431
Köning, A., Satt, A., Sorin, A., Hoory, R., Toledo-Ronen, O., Derreumaux, A., Manera, V., Verhey, F., Aalten, P., Robert, P.H. and David, R. (2015). Automatic speech analysis for the assessment of patients with predementia and Alzheimer’s disease. Alzheimer’s & Dementia: Diagnosis, Assessment & Disease Monitoring, 1(1), 112-124. https://doi.org/10.1016/j.dadm.2014.11.012
Koo, J., Lee, J. H., Pyo, J., Jo, Y., & Lee, K. (2020). Exploiting multi-modal features from pre-trained networks for Alz-heimer’s dementia recognition. arXiv preprint arXiv:2009.04070. https://doi.org/10.21437/Interspeech.2020-3153
Kumar, M. R., Vekkot, S., Lalitha, S., Gupta, D., Govindraj, V. J., Shaukat, K., … & Zakariah, M. (2022). Dementia De-tection from Speech Using Machine Learning and Deep Learning Architectures. Sensors, 22(23), 9311. https://doi.org/10.3390/s22239311
Kundaram, S. S., & Pathak, K. C. (2021). Deep learning-based Alzheimer’s disease detection. Proceedings of the Fourth International Conference on Microelectronics, Computing, and Communication Systems: MCCS 2019, 587-597. Springer Singapore. https://doi.org/10.1007/978-981-15-5546-6_50
Liu, L., Zhao, S., Chen, H., & Wang, A. (2020). A new machine learning method for identifying Alzheimer’s disease. Simulation Modelling Practice and Theory, 99, 102023. https://doi.org/10.1016/j.simpat.2019.102023
Liu, Z., Guo, Z., Ling, Z., & Li, Y. (2021). Detecting Alzheimer’s disease from speech using neural networks with bottleneck features and data augmentation. ICASSP 2021-2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 7323-7327. IEEE. https://doi.org/10.1109/ICASSP39728.2021.9413566
Meghanani, A., Anoop, C. S., & Ramakrishnan, A. G. (2021). An exploration of log-mel spectrogram and MFCC features for Alzheimer’s dementia recognition from spontaneous speech. 2021 IEEE Spoken Language Technology Workshop (SLT), 670-677. IEEE. https://doi.org/10.1109/SLT48900.2021.9383491
Mittal, A., Sahoo, S., Datar, A., Kadiwala, J., Shalu, H., & Mathew, J. (2020). Multi-modal detection of Alzheimer’s dis-ease from speech and text. arXiv preprint arXiv:2012.00096.
Orimaye, S. O., Wong, J. S. M., & Wong, C. P. (2018). Deep language space neural network for classifying mild cogni-tive impairment and Alzheimer-type dementia. PloS one, 13(11), e0205636. https://doi.org/10.1371/journal.pone.0205636
Pan, Y., Mirheidari, B., Reuber, M., Venneri, A., Blackburn, D., & Christensen, H. (2020). Improving detection of Alzheimer’s Disease using automatic speech recognition to identify high-quality segments for more robust feature extraction. Proceedings of Interspeech 2020, 4961-4965. International Speech Communication Association (ISCA). https://doi.org/10.21437/Interspeech.2020-2698
Pan, Y., Mirheidari, B., Harris, J. M., Thompson, J. C., Jones, M., Snowden, J. S., … & Christensen, H. (2021). Using the Outputs of Different Automatic Speech Recognition Paradigms for Acoustic-and BERT-Based Alzheimer’s Dementia Detection Through Spontaneous Speech. Interspeech, 3810-3814. https://doi.org/10.21437/Interspeech.2021-1519
Pappagari, R., Cho, J., Joshi, S., Moro-Velázquez, L., Zelasko, P., Villalba, J., & Dehak, N. (2021). Automatic Detection and Assessment of Alzheimer’s Disease Using Speech and Language Technologies in Low-Resource Scenarios. Interspeech, 3825-3829. https://doi.org/10.21437/Interspeech.2021-1850
Searle, T., Ibrahim, Z., & Dobson, R. (2020). Comparing natural language processing techniques for Alzheimer’s dementia prediction in spontaneous speech. arXiv preprint arXiv:2006.07358. https://doi.org/10.21437/Interspeech.2020-2729
Tóth, L., Hoffmann, I., Gosztolya, G., Vincze, V., Szatlóczki, G., Bánréti, Z., … & Kálmán, J. (2018). A speech recogni-tion-based solution for automatically detecting mild cognitive impairment from spontaneous speech. Current Alzheimer Research, 15(2), 130-138. https://doi.org/10.2174/1567205014666171121114930
Wang, N., Cao, Y., Hao, S., Shao, Z., & Subbalakshmi, K. P. (2021). Modular Multi-Modal Attention Network for Alzheimer’s Disease Detection Using Patient Audio and Language Data. Interspeech, 3835-3839. https://doi.org/10.21437/Interspeech.2021-2024
Warnita, T., Inoue, N., & Shinoda, K. (2018). Detecting Alzheimer’s disease using gated convolutional neural network from audio data. arXiv preprint arXiv:1803.11344. https://doi.org/10.21437/Interspeech.2018-1713
Xue, C., Karjadi, C., Paschalidis, I. C., Au, R., & Kolachalama, V. B. (2021). Detection of dementia on voice recordings using deep learning: a Framingham Heart Study. Alzheimer’s Research & Therapy, 13, 1-15. https://doi.org/10.1186/s13195-021-00888-3
Zargarbashi, S., & Babaali, B. (2019). A multi-modal feature embedding approach to diagnose Alzheimer’s disease from spoken language. arXiv preprint arXiv:1910.00330.
Tripathi, T., & Kumar, R. (2024). ML-Based Quantitative Analysis of Linguistic and Speech Features Relevant in Predicting Alzheimer’s Disease. ADCAIJ: Advances in Distributed Computing and Artificial Intelligence Journal, 13(1), e31625. https://doi.org/10.14201/adcaij.31625
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