References
- Hindle, J. V. (2010). Ageing, neurodegeneration and Parkinson's disease. Age and Ageing, 39 (2), 156-161. https://doi.org/10.1093/ageing/afp223
- Gorman, A. M. (2008). Neuronal cell death in neurodegenerative diseases: Recurring themes around protein handling. Journal of Cellular and Molecular Medicine, 12 (6a), 2263-2280. https://doi.org/10.1111%2Fj.1582-4934.2008.00402.x
- Damier, P., Hirsch, E. C., Agid, Y., Graybiel, A. M. (1999). The substantia nigra of the human brain. II. Patterns of loss of dopamine-containing neurons in Parkinson's disease. Brain, 122 (8), 1437-1448. https://doi.org/10.1093/brain/122.8.1437
- Reeve, A., Simcox, E., Turnbull, D. (2014). Ageing and Parkinson's disease: Why is advancing age the biggest risk factor? Ageing Research Reviews, 14, 19-30. https://doi.org/10.1016/j.arr.2014.01.004
- Moor, M., Banerjee, O., Abad, Z. S. H., Krumholz, H. M., Leskovec, J., Topol, E. J., Rajpurkar, P. (2023). Foundation models for generalist medical artificial intelligence. Nature, 616 (7956), 259-265. https://doi.org/10.1038/s41586-023-05881-4
- Rusz, J., Hlavnička, J., Tykalová, T., Bušková, J., Ulmanová, O., Růžička, E., Šonka, K. (2016). Quantitative assessment of motor speech abnormalities in idiopathic rapid eye movement sleep behaviour disorder. Sleep Medicine, 19, 141-147. https://doi.org/10.1016/j.sleep.2015.07.030
- Tykalova, T., Rusz, J., Cmejla, R., Ruzickova, H., Ruzicka, E. (2014). Acoustic investigation of stress patterns in Parkinson's disease. Journal of Voice, 28 (1), 129.e1-129.e8. https://doi.org/10.1016/j.jvoice.2013.07.001
- Tjaden, K. (2008). Speech and swallowing in Parkinson's disease. Topics in Geriatric Rehabilitation, 24 (2), 115-126. https://doi.org/10.1097%2F01.TGR.0000318899.87690.44
- Nilashi, M., Ibrahim, O., Ahmadi, H., Shahmoradi, L., Farahmand, M. (2018). A hybrid intelligent system for the prediction of Parkinson's Disease progression using machine learning techniques. Biocybernetics and Biomedical Engineering, 38 (1), 1-15. https://doi.org/10.1016/j.bbe.2017.09.002
- Novotny, M., Rusz, J., Cmejla, R., Ruzicka, E. (2014). Automatic evaluation of articulatory disorders in Parkinson’s disease. IEEE/ACM Transactions on Audio, Speech, and Language Processing, 22 (9), 1366-1378. https://doi.org/10.1109/TASLP.2014.2329734
- Dimauro, G., Di Nicola, V., Bevilacqua, V., Caivano, D., Girardi, F. (2017). Assessment of speech intelligibility in Parkinson’s disease using a speech-to-text system. IEEE Access, 5, 22199-22208. https://doi.org/10.1109/ACCESS.2017.2762475
- Kwasny, D., Hemmerling, D. (2021). Gender and age estimation methods based on speech using deep neural networks. Sensors, 21 (14), 4785. https://doi.org/10.3390/s21144785
- Sánchez-Hevia, H. A., Gil-Pita, R., Utrilla-Manso, M., Rosa-Zurera, M. (2022). Age group classification and gender recognition from speech with temporal convolutional neural networks. Multimedia Tools and Applications, 81 (3), 3535-3552. https://doi.org/10.1007/s11042-021-11614-4
- Jeancolas, L., Mangone, G., Petrovska-Delacrétaz, D., Benali, H., Benkelfat, B.-E., Arnulf, I., Corvol, J.-C., Vidailhet, M., Lehéricy, S. (2022). Voice characteristics from isolated rapid eye movement sleep behavior disorder to early Parkinson's disease. Parkinsonism & Related Disorders, 95, 86-91. https://doi.org/10.1016/j.parkreldis.2022.01.003
- Boersma, P., Weenink, D. Praat: Doing phonetics by computer. https://www.fon.hum.uva.nl/praat/
- Rusz, J., Tykalová, T., Novotný, M., Zogala, D., Růžička, E., Dušek, P. (2022). Automated speech analysis in early untreated Parkinson's disease: Relation to gender and dopaminergic transporter imaging. European Journal of Neurology, 29 (1), 81-90. https://doi.org/10.1111/ene.15099
- Rusz, J., Hlavnička, J., Novotný, M., Tykalová, T., Pelletier, A., Montplaisir, J., Gagnon, J.-F., Dušek, P., Galbiati, A., Marelli, S., Timm, P. C., Teigen, L. N., Janzen, A., Habibi, M., Stefani, A., Holzknecht, E., Seppi, K., Evangelista, E., Rassu, A. L., Dauvilliers, Y., Högl, B., Oertel, W., St. Louis, E. K., Ferini-Strambi, L., Růžička, E., Postuma, R. B., Šonka, K. (2021). Speech biomarkers in rapid eye movement sleep behavior disorder and Parkinson disease. Annals of Neurology, 90 (1), 62-75. https://doi.org/10.1002/ana.26085
- Rusz, J., Tykalova, T., Novotny, M., Zogala, D., Sonka, K., Ruzicka, E., Dusek, P. (2021). Defining speech subtypes in de novo Parkinson disease. Neurology, 97 (21), e2124-e2135. https://doi.org/10.1212/WNL.0000000000012878
- Issa, D., Fatih Demirci, M., Yazici, A. (2020) Speech emotion recognition with deep convolutional neural networks. Biomedical Signal Processing and Control, 59, 101894. https://doi.org/10.1016/j.bspc.2020.101894
- Tuncer, T., Dogan, S., Acharya, U. R. (2020). Automated detection of Parkinson's disease using minimum average maximum tree and singular value decomposition method with vowels. Biocybernetics and Biomedical Engineering, 40 (1), 211-220. https://doi.org/10.1016/j.bbe.2019.05.006
- Karan, B., Sahu, S. S., Mahto, K. (2020). Parkinson disease prediction using intrinsic mode function based features from speech signal. Biocybernetics and Biomedical Engineering, 40 (1), 249-264. https://doi.org/10.1016/j.bbe.2019.05.005
- Solana-Lavalle, G., Galán-Hernández, J.-C., Rosas-Romero, R. (2020). Automatic Parkinson disease detection at early stages as a pre-diagnosis tool by using classifiers and a small set of vocal features. Biocybernetics and Biomedical Engineering, 40 (1), 505-516. https://doi.org/10.1016/j.bbe.2020.01.003
- Castro, C., Vargas-Viveros, E., Sánchez, A., Gutiérrez-López, E., Flores, D.-L. (2020). Parkinson’s disease classification using artificial neural networks. In VIII Latin American Conference on Biomedical Engineering and XLII National Conference on Biomedical Engineering. Springer, IFMBE Proceedings 75, 1060-1065. https://doi.org/10.1007/978-3-030-30648-9_137
- Schneider, S., Baevski, A., Collobert, R., Auli, M. (2019). wav2vec: Unsupervised pre-training for speech recognition. arXiv, https://arxiv.org/abs/1904.05862.
- Riviere, M., Joulin, A., Mazare, P.-E., Dupoux, E. (2020). Unsupervised pretraining transfers well across languages. In ICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing. IEEE, 7414-7418. https://doi.org/10.1109/ICASSP40776.2020.9054548
- Mikolov, T., Chen, K., Corrado, G., Dean, J. (2013). Efficient estimation of word representations in vector space. arXiv, https://arxiv.org/abs/1301.3781.
- Hannigan, G. D., Prihoda, D., Palicka, A., Soukup, J., Klempir, O., Rampula, L., Durcak, J., Wurst, M., Kotowski, J., Chang, D., Wang, R., Piizzi, G., Temesi, G., Hazuda, D. J., Woelk, C. H., Bitton, D. A. (2019). A deep learning genome-mining strategy for biosynthetic gene cluster prediction. Nucleic Acids Research, 47 (18), e110. https://doi.org/10.1093/nar/gkz654
- Baevski, A., Mohamed, A. (2020). Effectiveness of self-supervised pre-training for ASR. In ICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing. IEEE, 7694-7698. https://doi.org/10.1109/ICASSP40776.2020.9054224
- Bayerl, S. P., Wagner, D., Baumann, I., Bocklet, T., Riedhammer, K. (2023). Detecting vocal fatigue with neural embeddings. Journal of Voice. https://doi.org/10.1016/j.jvoice.2023.01.012
- Švec, J., Polák, F., Bartoš, A., Zapletalová, M., Víta, M. (2022). Evaluation of Wav2Vec speech recognition for speakers with cognitive disorders. In Text, Speech, and Dialogue: 25th International Conference (TSD 2022). Springer, LNAI 13502, 501-512. https://doi.org/10.1007/978-3-031-16270-1_41
- Hernandez, A., Pérez-Toro, P. A., Nöth, E., Orozco-Arroyave, J. R., Maier, A., Yang, S. H. (2022). Cross-lingual self-supervised speech representations for improved dysarthric speech recognition. arXiv, https://arxiv.org/abs/2204.01670.
- Escobar-Grisales, D., Ríos-Urrego, C. D., Orozco-Arroyave, J. R. (2023). Deep learning and artificial intelligence applied to model speech and language in Parkinson’s disease. Diagnostics, 13 (13), 2163. https://doi.org/10.3390/diagnostics13132163
- Klempir, O., Krupicka, R. (2018). Machine learning using speech utterances for Parkinson disease detection. Lékař a Technika / Clinician and Technology, 48 (2), 66-71. https://ojs.cvut.cz/ojs/index.php/CTJ/article/view/4881
- Jaeger, H., Trivedi, D., Stadtschnitzer, M. (2019). Mobile Device Voice Recordings at King's College London (MDVR-KCL) from both early and advanced Parkinson's disease patients and healthy controls. Zenodo, https://zenodo.org/doi/10.5281/zenodo.2867215.
- Ott, M., Edunov, S., Baevski, A., Fan, A., Gross, S., Ng. N., Grangier, D., Fairseq, M. A. (2019). fairseq: A fast, extensible toolkit for sequence modeling. In Proceedings of NAACL-HLT 2019: Demonstrations. https://github.com/facebookresearch/fairseq
- wav2vec large.pt https://dl.fbaipublicfiles.com/fairseq/wav2vec/wav2vec_large.pt
- Lehecka, J., Svec, J., Prazak, A., Psutka, J. V. (2022). Exploring capabilities of monolingual audio transformers using large datasets in automatic speech recognition of Czech. arXiv, https://arxiv.org/abs/2206.07627.
- Klempíř, O. (2023). Evaluating the performance of wav2vec embedding for Parkinson's disease detection. GitLab, https://gitlab.fel.cvut.cz/klempond/wav2vec-embedding-for-pd-detection/.
- Klempíř, O., Krupička, R., Bakštein, E., Jech, R. (2019). Identification of microrecording artifacts with wavelet analysis and convolutional neural network: An image recognition approach. Measurement Science Review, 19 (5), 222-231. https://doi.org/10.2478/msr-2019-0029
- Toye, A. A., Kompalli, S. (2021). Comparative study of speech analysis methods to predict Parkinson's disease. arXiv, https://arxiv.org/abs/2111.10207.
- Rusz, J., Tykalová, T., Novotný, M., Růžička, E., Dušek, P. (2021). Distinct patterns of speech disorder in early-onset and late-onset de-novo Parkinson’s disease. npj Parkinson's Disease 7, 98. https://doi.org/10.1038/s41531-021-00243-1