Abstract
One of the world’s chronic neuro-degenerative diseases, Alzheimer’s Disease (AD), leads its sufferers, among other symptoms, to suffer from speech difficulties. In particular, the inability to recall vocabulary which makes patients’ speech different. Furthermore, Mild Cognitive Impairment (MCI) is usually considered as a prodromal neuro-degenerative state of AD. The key to abate the progress of both disorders is their early diagnosis. However, actual ways of diagnosis are costly and quite time-consuming. In this study, we propose the extraction of features from speech through the use of the i-vector approach, by which we seek to model the speech pattern of the three mental conditions from the subjects. To the best of our knowledge, no previous studies have utilized i-vector features to assess Alzheimer’s before. These i-vectors are extracted from Mel-Frequency Cepstral Coefficients (MFCCs), then they are given to a SVM classifier in order to identify the speech in one of the following manners: AD - Alzheimer Disease, MCI - Mild Cognitive Impairment, HC - Healthy Control. We tested these i-vector features by performing a 5-fold cross-validation and we achieved an F1-score of 79.2%.
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Acknowledgments
This study was partially funded by the National Research, Development and Innovation Office of Hungary via contract NKFIH FK-124413 and by the Ministry of Human Capacities, Hungary (grant 20391-3/2018/FEKUSTRAT). László Tóth was supported by the János Bolyai Research Scholarship of the Hungarian Academy of Sciences.
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Egas López, J.V., Tóth, L., Hoffmann, I., Kálmán, J., Pákáski, M., Gosztolya, G. (2019). Assessing Alzheimer’s Disease from Speech Using the i-vector Approach. In: Salah, A., Karpov, A., Potapova, R. (eds) Speech and Computer. SPECOM 2019. Lecture Notes in Computer Science(), vol 11658. Springer, Cham. https://doi.org/10.1007/978-3-030-26061-3_30
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