Järve, Joonas, juhendajaTaba, Pille, juhendajaKrass, AleksisTartu Ülikool. Loodus- ja täppisteaduste valdkondTartu Ülikool. Arvutiteaduse instituut2025-10-202025-10-202025https://hdl.handle.net/10062/116869This Bachelor’s thesis investigates the applicability of machine learning methods for Parkinson’s Disease (PD) detection using Estonian voice clips. The research focuses on three main questions: firstly, evaluating the generalizability of an acoustic feature-based model trained on the Spanish PC-GITA dataset to Estonian data; secondly, examining whether combining Spanish and Estonian data during training improves model performance; and thirdly, testing the direct applicability of a state-of-the-art self-supervised learning (SSL) based WavLM Base model, fine-tuned elsewhere, on Estonian data. The results indicate that the direct cross-lingual transferability of acoustic feature-based models is limited, but combining datasets significantly improves performance up to 0.7893. The direct application of a pre-fine-tuned SSL model on short Estonian audio segments without further adaptation was not successful. The thesis highlights the need for language-specific adaptation and the use of multilingual datasets in voice-based PD detection.ethttps://creativecommons.org/licenses/by-nc-nd/4.0/Parkinsoni tõbimasinõpeaudiohääleanalüüssüvaõpesiirdeõpeakustilised tunnusedeesti keelbakalaureusetöödinformaatikainfotehnoloogiainformaticsinfotechnologyThesis