Berus L, Klancnik S, Brezocnik M, Ficko M. Classifying parkinson’s disease based on acoustic measures using artificial neural networks. Sens (Switzerland). 2019;19. https://doi.org/10.3390/S19010016.

Naseer A, Rani M, Naz S, Razzak MI, Imran M, Xu G. Refining Parkinson’s neurological disorder identification through deep transfer learning. Neural Comput Appl. 2020;32:839–54. https://doi.org/10.1007/S00521-019-04069-0.

Article  Google Scholar 

Arena JE, Stoessl AJ. Optimizing diagnosis in Parkinson’s disease: Radionuclide imaging. Parkinsonism Relat Disord. 2016;22:S47–51. https://doi.org/10.1016/J.PARKRELDIS.2015.09.029.

Article  MATH  Google Scholar 

Ngo QC, Motin MA, Pah ND, Drotár P, Kempster P, Kumar D. Computerized analysis of speech and voice for Parkinson’s disease: a systematic review. Comput Methods Programs Biomed. 2022;226:107133. https://doi.org/10.1016/J.CMPB.2022.107133.

Article  Google Scholar 

Arias-Londoño Julián JA D. and, Gómez-García. Predicting UPDRS scores in Parkinson’s Disease using Voice signals: a deep Learning/Transfer-Learning-based Approach. In: Godino-Llorente JI, editor. Automatic Assessment of Parkinsonian Speech. Cham: Springer International Publishing; 2020. pp. 100–23. https://doi.org/10.1007/978-3-030-65654-6_6.

Chapter  MATH  Google Scholar 

Arias-Vergara T, Vasquez-Correa JC, Orozco-Arroyave JR, Klumpp P, Nöth E. Unobtrusive Monitoring of Speech Impairments of Parkinson’S Disease Patients Through Mobile Devices, in: 2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2018: pp. 6004–6008. https://doi.org/10.1109/ICASSP.2018.8462332

Suppa A, Costantini G, Asci F, Di Leo P, Al-Wardat MS, Di Lazzaro G, Scalise S, Pisani A, Saggio G. Voice in Parkinson’s disease: a machine learning study. Front Neurol. 2022;13:831428. https://doi.org/10.3389/fneur.2022.831428.

Article  Google Scholar 

Templeton JM, Poellabauer C, Schneider S. Classification of Parkinson’s disease and its stages using machine learning. Sci Rep 2022. 2022;12(12):1. https://doi.org/10.1038/s41598-022-18015-z.

Article  MATH  Google Scholar 

Hsu SY, Yeh LR, Chen TB, Du WC, Huang YH, Twan WH, Lin MC, Hsu YH, Wu YC, Chen HY. Classification of the multiple stages of Parkinson’s disease by a deep convolution neural network based on 99mTc-TRODAT-1 SPECT images. Molecules. 2020;25. https://doi.org/10.3390/MOLECULES25204792.

Zheng X, Phukon B, Hasegawa-Johnson M. Fine-tuning Automatic Speech Recognition for People with Parkinson’s: An Effective Strategy for Enhancing Speech Technology Accessibility, Interspeech 2024, 1–5 September 2024, Kos, Greece.

Radford A, Kim JW, Xu T, Brockman G, McLeavey C, Sutskever I. Robust Speech Recognition via large-scale weak Supervision. Proc Mach Learn Res. 2022;202:28492–518. https://arxiv.org/abs/2212.04356v1.

Google Scholar 

Baevski A, Zhou H, Mohamed A, Auli M. wav2vec 2.0: a framework for self-supervised learning of speech representations. Adv Neural Inf Process Syst 2020-December (2020).

Hoehn MM, Yahr MD. Parkinsonism: onset, progression and mortality. Neurology. 1967;17:427–427. https://doi.org/10.1212/WNL.17.5.427.

Article  MATH  Google Scholar 

Ozbolt AS, Moro-Velazquez L, Lina I, Butala AA, Dehak N. Things to Consider When Automatically Detecting Parkinson’s Disease Using the Phonation of Sustained Vowels: Analysis of Methodological Issues, Applied Sciences 2022, Vol. 12, Page 991 12 (2022) 991. https://doi.org/10.3390/APP12030991

Hughes AJ, Daniel SE, Kilford L, Lees AJ. Accuracy of clinical diagnosis of idiopathic Parkinson’s disease: a clinico-pathological study of 100 cases. J Neurol Neurosurg Psychiatry. 1992;55:181–4. https://doi.org/10.1136/JNNP.55.3.181.

Article  Google Scholar 

Omberg L, Chaibub Neto E, Perumal TM, Pratap A, Tediarjo A, Adams J, Bloem BR, Bot BM, Elson M, Goldman SM, Kellen MR, Kieburtz K, Klein A, Little MA, Schneider R, Suver C, Tarolli C, Tanner CM, Trister AD, Wilbanks J, Dorsey ER, Mangravite LM. Remote smartphone monitoring of Parkinson’s disease and individual response to therapy. Nat Biotechnol. 2022;40:480–7. https://doi.org/10.1038/s41587-021-00974-9.

Article  Google Scholar 

P. Faragó, S.-A. Ștefănigă, C.-G. Cordoș, L.-I. Mihăilă, S. Hintea, A.-S. Peștean,M. Beyer, L. Perju-Dumbravă, R.R. Ileșan, CNN-Based Identification of Parkinson’s Disease from Continuous Speech in Noisy Environments, Bioengineering 2023, Vol. 10,Page 531 10 (2023) 531. https://doi.org/10.3390/bioengineering10050531

Asci F, Costantini G, Di Leo P, Zampogna A, Ruoppolo G, Berardelli A, Saggio G, Suppa A. Machine-Learning Analysis of Voice Samples Recorded through Smartphones: The Combined Effect of Ageing and Gender, Sensors 2020, Vol. 20, Page 5022 20 (2020) 5022. https://doi.org/10.3390/S20185022

Vaiciukynas E, Verikas A, Gelzinis A, Bacauskiene M. Detecting Parkinson’s disease from sustained phonation and speech signals. PLoS ONE. 2017;12:e0185613. https://doi.org/10.1371/JOURNAL.PONE.0185613.

Article  MATH  Google Scholar 

Benba A, Jilbab A, Hammouch A. Detecting patients with Parkinson ’ s disease using Mel frequency Cepstral coefficients and Support Vector machines. Int J Electr Eng Inf. 2015;7:297–307.

Google Scholar 

Moro-Velazquez L, Gomez-Garcia JA, Arias-Londoño JD, Dehak N, Godino-Llorente JI. Advances in Parkinson’s Disease detection and assessment using voice and speech: a review of the articulatory and phonatory aspects. Biomed Signal Process Control. 2021;66:102418. https://doi.org/10.1016/j.bspc.2021.102418.

Article  MATH  Google Scholar 

Boersma P. Praat: doing phonetics by computer. Ear Hear. 2011;32:266. https://doi.org/10.1097/AUD.0b013e31821473f7.

Article  MATH  Google Scholar 

Jadoul Y, Thompson B, de Boer B. Introducing parselmouth: a Python interface to Praat. J Phon. 2018;71:1–15. https://doi.org/10.1016/J.WOCN.2018.07.001.

Article  Google Scholar 

Pedregosa F, Michel FABIANPEDREGOSAV, Grisel O, Blondel OLIVIERGRISELM, Prettenhofer P, Weiss R, Vanderplas J, Cournapeau D, Pedregosa F, Varoquaux G, Gramfort A, Thirion B, Grisel O, Dubourg V, Passos A, Brucher M. Perrot andÉdouardand, andÉdouard Duchesnay, Fré. Duchesnay EDOUARDDUCHESNAY, Scikit-learn: machine learning in Python. J Mach Learn Res. 2011;12:2825–30.

MathSciNet  MATH  Google Scholar 

Chawla NV, Bowyer KW, Hall LO, Kegelmeyer WP. Synthetic minority over-sampling technique. J Artif Intell Res. 2002;16:321–57. https://doi.org/10.1613/JAIR.953.

Article  MATH  Google Scholar 

Japkowicz N, Stephen S. The class imbalance problem: a systematic study. Intell Data Anal. 2002;6:429–49. https://doi.org/10.3233/IDA-2002-6504.

Article  MATH  Google Scholar 

Filippidou F, Moussiades L. Α benchmarking of IBM, Google and wit automatic speech recognition systems, IFIP Adv Inf Commun Technol 583 IFIP (2020) 73–82. https://doi.org/10.1007/978-3-030-49161-1_7/TABLES/4