Personalized Text-to-Speech Solutions for Parkinson’s Patients through Generative AI
Abstract
Parkinson’s disease significantly impairs speech, leaving patients struggling with reduced vocal clarity, diminished volume, and monotonous tone that hinder effective communication. These challenges often extend beyond physical symptoms, causing social isolation, loss of confidence, and emotional distress. Traditional speech therapies and assistive devices provide some relief but frequently fail to restore the personal and natural qualities of an individual’s voice. Generative Artificial Intelligence (GenAI) introduces a new dimension of support by enabling highly personalized text-to-speech (TTS) solutions that replicate or enhance a patient’s voice in ways that conventional technologies cannot achieve. This paper explores how GenAI-powered TTS systems can be tailored for Parkinson’s patients, examining their role in clinical settings, daily life, and social integration. It discusses the technical foundations of voice cloning, adaptive speech synthesis, and natural language processing while highlighting the medical benefits of improved communication in therapy and consultations. The analysis also addresses the emotional and psychological impact of restoring a patient’s unique vocal identity, which strengthens self-esteem and nurtures meaningful human connections. Challenges related to accessibility, ethical considerations, and data security are considered, alongside future directions such as multimodal communication, brain–computer interfaces, and integration with telemedicine. Ultimately, personalized TTS solutions represent more than just technological tools; they are lifelines that empower patients to preserve their dignity, maintain independence, and reconnect with the world. By combining medical insight with technological innovation, GenAI offers a transformative approach to Parkinson’s care—restoring not only speech but also the human experience of being heard.
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Velázquez-Paniagua, M., et al., Current treatments in Parkinson's including the proposal of an innovative dopamine microimplant. Revista Médica del Hospital General de México, 2016. 79(2): p. 79-87.
Ghane, M., et al., Enhanced decision tree induction using evolutionary techniques for Parkinson's disease classification. Biocybernetics and Biomedical Engineering, 2022. 42(3): p. 902-920.
Saint-Hilaire, M.-H. and C.-A. Thomas. Delivering a Diagnosis of Parkinson's Disease and Parkinsonism with Wisdom and Sensitivity. in Seminars in Neurology. 2023. Thieme Medical Publishers, Inc.
Goldenberg, M.M., Medical management of Parkinson’s disease. Pharmacy and Therapeutics, 2008. 33(10): p. 590.
Halkias, I.A.C., et al., When Should Levodopa Therapy be Initiated in Patients with Parkinson’s Disease? Drugs & Aging, 2007. 24(4): p. 261-273.
Abumalloh, R.A., et al., Parkinson’s disease diagnosis using deep learning: A bibliometric analysis and literature review. Ageing Research Reviews, 2024. 96: p. 102285.
Shokeen, A., et al., Advancement in herbal drugs for the treatment of Parkinson’s disease, in Targeting Angiogenesis, Inflammation, and Oxidative Stress in Chronic Diseases. 2024, Elsevier. p. 251-276.
Herd, C.P., et al., Speech and language therapy versus placebo or no intervention for speech problems in Parkinson's disease. Cochrane Database of Systematic Reviews, 1996. 2012(8).
AlZubi, A.A., A. Alarifi, and M. Al-Maitah, Deep brain simulation wearable IoT sensor device based Parkinson brain disorder detection using heuristic tubu optimized sequence modular neural network. Measurement, 2020. 161: p. 107887.
Caliskan, A., et al., Diagnosis of the parkinson disease by using deep neural network classifier. IU-Journal of Electrical & Electronics Engineering, 2017. 17(2): p. 3311-3318.
Haq, A.U., et al. Comparative analysis of the classification performance of machine learning classifiers and deep neural network classifier for prediction of Parkinson disease. in 2018 15th international computer conference on wavelet active media technology and information processing (ICCWAMTIP). 2018. IEEE.
Manap, H.H., N.M. Tahir, and A.I.M. Yassin. Statistical analysis of parkinson disease gait classification using Artificial Neural Network. in 2011 IEEE International Symposium on Signal Processing and Information Technology (ISSPIT). 2011. IEEE.
Muniz, A., et al., Comparison among probabilistic neural network, support vector machine and logistic regression for evaluating the effect of subthalamic stimulation in Parkinson disease on ground reaction force during gait. Journal of biomechanics, 2010. 43(4): p. 720-726.
Pedrero-Sánchez, J.F., et al., Classification of healthy, Alzheimer and Parkinson populations with a multi-branch neural network. Biomedical Signal Processing and Control, 2022. 75: p. 103617.
Shah, P.M., et al. Detection of Parkinson disease in brain MRI using convolutional neural network. in 2018 24th international conference on automation and computing (ICAC). 2018. IEEE.
Zhang, Y.N., Can a smartphone diagnose parkinson disease? a deep neural network method and telediagnosis system implementation. Parkinson’s disease, 2017. 2017(1): p. 6209703.
Nilashi, M., et al., Knowledge discovery of patients reviews on breast cancer drugs: Segmentation of side effects using machine learning techniques. Heliyon, 2024. 10(19).
Abumalloh, R.A., et al., Impact of generative artificial intelligence models on the performance of citizen data scientists in retail firms. Computers in Industry, 2024. 161: p. 104128.
Mittal, C., S. Debnath, and S. Prabakeran, Customized treatment plan using GenAI for cardiological diseases, in Challenges in Information, Communication and Computing Technology. 2025, CRC Press. p. 87-91.
Chiu, E.K.-Y., et al. Generative artificial intelligence models in clinical infectious disease consultations: a cross-sectional analysis among specialists and resident trainees. in Healthcare. 2025. MDPI.
Ahmadi, H., et al., Diseases diagnosis using fuzzy logic methods: A systematic and meta-analysis review. Computer Methods and Programs in Biomedicine, 2018. 161: p. 145-172.
Nilashi, M., et al., Predicting parkinson’s disease progression: Evaluation of ensemble methods in machine learning. Journal of healthcare engineering, 2022. 2022(1): p. 2793361.
Nilashi, M., et al., Early diagnosis of Parkinson’s disease: A combined method using deep learning and neuro-fuzzy techniques. Computational biology and chemistry, 2023. 102: p. 107788.
Nilashi, M., et al., A predictive method for hepatitis disease diagnosis using ensembles of neuro-fuzzy technique. Journal of infection and public health, 2019. 12(1): p. 13-20.
Nilashi, M., et al., Remote tracking of Parkinson's disease progression using ensembles of deep belief network and self-organizing map. Expert Systems with Applications, 2020. 159: p. 113562.
Nilashi, M., et al., A soft computing approach for diabetes disease classification. Health Informatics Journal, 2018. 24(4): p. 379-393.
Nilashi, M., et al., A hybrid intelligent system for the prediction of Parkinson's Disease progression using machine learning techniques. Biocybernetics and Biomedical Engineering, 2018. 38(1): p. 1-15.
Nilashi, M., et al., An analytical method for measuring the Parkinson’s disease progression: A case on a Parkinson’s telemonitoring dataset. Measurement, 2019. 136: p. 545-557.
Bhattacharya, I. and M.P.S. Bhatia, SVM classification to distinguish Parkinson disease patients, in Proceedings of the 1st Amrita ACM-W Celebration on Women in Computing in India. 2010. p. 1-6.
Ozcift, A., SVM feature selection based rotation forest ensemble classifiers to improve computer-aided diagnosis of Parkinson disease. Journal of medical systems, 2012. 36(4): p. 2141-2147.
Eskidere, Ö., F. Erta?, and C. Hanilçi, A comparison of regression methods for remote tracking of Parkinson’s disease progression. Expert Systems with Applications, 2012. 39(5): p. 5523-5528.
Chen, H.-L., et al., An efficient diagnosis system for detection of Parkinson’s disease using fuzzy k-nearest neighbor approach. Expert systems with applications, 2013. 40(1): p. 263-271.
Behroozi, M. and A. Sami, A multiple-classifier framework for Parkinson’s disease detection based on various vocal tests. International journal of telemedicine and applications, 2016. 2016.
Asgari, M. and I. Shafran. Extracting cues from speech for predicting severity of parkinson's disease. in 2010 IEEE International Workshop on Machine Learning for Signal Processing. 2010. IEEE.
Yadav, G., Y. Kumar, and G. Sahoo. Predication of Parkinson's disease using data mining methods: A comparative analysis of tree, statistical and support vector machine classifiers. in 2012 National Conference on Computing and Communication Systems. 2012. IEEE.
Polat, K., Classification of Parkinson's disease using feature weighting method on the basis of fuzzy C-means clustering. International Journal of Systems Science, 2012. 43(4): p. 597-609.
Jain, S. and S. Shetty. Improving accuracy in noninvasive telemonitoring of progression of Parkinson'S Disease using two-step predictive model. in 2016 Third International Conference on Electrical, Electronics, Computer Engineering and their Applications (EECEA). 2016. IEEE.
Wan, S., et al., Deep multi-layer perceptron classifier for behavior analysis to estimate parkinson’s disease severity using smartphones. IEEE Access, 2018. 6: p. 36825-36833.
Das, R., A comparison of multiple classification methods for diagnosis of Parkinson disease. Expert Systems with Applications, 2010. 37(2): p. 1568-1572.
Afonso, L.C., et al., A recurrence plot-based approach for Parkinson’s disease identification. Future Generation Computer Systems, 2019. 94: p. 282-292.
Pereira, C.R., et al., Handwritten dynamics assessment through convolutional neural networks: An application to Parkinson's disease identification. Artificial intelligence in medicine, 2018. 87: p. 67-77.
Shinde, S., et al., Predictive markers for Parkinson's disease using deep neural nets on neuromelanin sensitive MRI. Neuroimage: Clinical, 2019. 22: p. 101748.
Babu, G.S. and S. Suresh, Parkinson’s disease prediction using gene expression–A projection based learning meta-cognitive neural classifier approach. Expert Systems with Applications, 2013. 40(5): p. 1519-1529.
Hariharan, M., K. Polat, and R. Sindhu, A new hybrid intelligent system for accurate detection of Parkinson's disease. Computer methods and programs in biomedicine, 2014. 113(3): p. 904-913.
Khan, M.M., S.K. Chalup, and A. Mendes. Parkinson’s disease data classification using evolvable wavelet neural networks. in Australasian Conference on Artificial Life and Computational Intelligence. 2016. Springer.
Buza, K. and N.Á. Varga, Parkinsonet: estimation of updrs score using hubness-aware feedforward neural networks. Applied Artificial Intelligence, 2016. 30(6): p. 541-555.
Al-Fatlawi, A.H., M.H. Jabardi, and S.H. Ling. Efficient diagnosis system for Parkinson's disease using deep belief network. in 2016 IEEE Congress on Evolutionary Computation (CEC). 2016. IEEE.
Grover, S., et al., Predicting severity of Parkinson’s disease using deep learning. Procedia computer science, 2018. 132: p. 1788-1794.
Li, D.-C., C.-W. Liu, and S.C. Hu, A fuzzy-based data transformation for feature extraction to increase classification performance with small medical data sets. Artificial Intelligence in Medicine, 2011. 52(1): p. 45-52.
Guo, P.-F., P. Bhattacharya, and N. Kharma. Advances in detecting Parkinson’s disease. in International Conference on Medical Biometrics. 2010. Springer.
Avci, D. and A. Dogantekin, An expert diagnosis system for parkinson disease based on genetic algorithm-wavelet kernel-extreme learning machine. Parkinson’s disease, 2016. 2016.
Nilashi, M., et al., An analytical method for diseases prediction using machine learning techniques. Computers & Chemical Engineering, 2017. 106: p. 212-223.
Peterek, T., et al. Performance evaluation of Random Forest regression model in tracking Parkinson's disease progress. in 13th International Conference on Hybrid Intelligent Systems (HIS 2013). 2013. IEEE.
Froelich, W., K. Wrobel, and P. Porwik, Diagnosis of Parkinson's disease using speech samples and threshold-based classification. Journal of Medical Imaging and Health Informatics, 2015. 5(6): p. 1358-1363.
Exarchos, T.P., et al., Using partial decision trees to predict Parkinson’s symptoms: A new approach for diagnosis and therapy in patients suffering from Parkinson’s disease. Computers in biology and medicine, 2012. 42(2): p. 195-204.
Naranjo, L., et al., Addressing voice recording replications for Parkinson’s disease detection. Expert Systems with Applications, 2016. 46: p. 286-292.
Abdar, M. and M. Zomorodi-Moghadam, Impact of patients’ gender on parkinson’s disease using classification algorithms. Journal of AI and Data Mining, 2018. 6(2): p. 277-285.
Gunduz, H., Deep Learning-Based Parkinson’s Disease Classification Using Vocal Feature Sets. IEEE Access, 2019. 7: p. 115540-115551.
Johri, A. and A. Tripathi. Parkinson Disease Detection Using Deep Neural Networks. in 2019 Twelfth International Conference on Contemporary Computing (IC3). 2019. IEEE.
Alghamdi, A., et al., Analysis of social data for accuracy improvement of collaborative filtering in MOOCs using text mining and deep learning techniques. Discover Computing, 2025. 28(1): p. 1-21.
Farokhi, M., et al., A Multi-Criteria Recommender System for Tourism Using Fuzzy Approach. Journal of Soft Computing & Decision Support Systems, 2016. 3(4).
Mardani, A., et al., A multi-stage method to predict carbon dioxide emissions using dimensionality reduction, clustering, and machine learning techniques. Journal of Cleaner Production, 2020. 275: p. 122942.
Nilashi, M., et al., A combined method for diabetes mellitus diagnosis using deep learning, singular value decomposition, and self-organizing map approaches. Diagnostics, 2023. 13(10): p. 1821.
Nilashi, M., et al., Factors impacting customer purchase intention of smart home security systems: Social data analysis using machine learning techniques. Technology in Society, 2022. 71: p. 102118.
Nilashi, M., et al., A Multi-Criteria Collaborative Filtering Recommender System Using Clustering and Regression Techniques. Journal of Soft Computing & Decision Support Systems, 2016. 3(5).
Nilashi, M., et al., Accuracy improvement for diabetes disease classification: a case on a public medical dataset. Fuzzy Information and Engineering, 2017. 9(3): p. 345-357.
Nilashi, M., et al., Clustering-and regression-based multi-criteria collaborative filtering with incremental updates. Information Sciences, 2015. 293: p. 235-250.
Ahmadi, N., et al., Eye State Identification Utilizing EEG Signals: A Combined Method Using Self?Organizing Map and Deep Belief Network. Scientific Programming, 2022. 2022(1): p. 4439189.
Nilashi, M., et al., The impact of multi-criteria ratings in social networking sites on the performance of online recommendation agents. Telematics and Informatics, 2023. 76: p. 101919.
Nilashi, M., et al., Customer satisfaction analysis and preference prediction in historic sites through electronic word of mouth. Neural Computing and Applications, 2022. 34(16): p. 13867-13881.
Nilashi, M., et al., Knowledge discovery for course choice decision in Massive Open Online Courses using machine learning approaches. Expert Systems with Applications, 2022. 199: p. 117092.
Akbari, E., et al., ANFIS modeling for bacteria detection based on GNR biosensor. Journal of Chemical Technology & Biotechnology, 2016. 91(6): p. 1728-1736.
Foroughi, B., et al., Determinants of intention to use autonomous vehicles: Findings from PLS-SEM and ANFIS. Journal of Retailing and Consumer Services, 2023. 70: p. 103158.
Iranmanesh, M., et al., Factors influencing attitude and intention to use autonomous vehicles in Vietnam: findings from PLS-SEM and ANFIS. Information Technology & People, 2024. 37(6): p. 2223-2246.
Kheirandish, A., et al., Using ANFIS technique for PEM fuel cell electric bicycle prediction model. International Journal of Environmental Science and Technology, 2019. 16(11): p. 7319-7326.
Nilashi, M., Evaluation of Security Pillars in the Industrial Internet of Things: A Fuzzy Logic Approach. Journal of Soft Computing and Decision Support Systems, 2024. 11(4): p. 1-7.
Nilashi, M., et al., Accuracy analysis of Type-2 fuzzy system in predicting parkinson’s disease using biomedical voice measures. International Journal of Fuzzy Systems, 2024. 26(4): p. 1261-1284.
Nilashi, M., et al., A hybrid method to solve data sparsity in travel recommendation agents using fuzzy logic approach. Mathematical Problems in Engineering, 2022. 2022(1): p. 7372849.
Nilashi, M., et al., Research Article A Hybrid Method to Solve Data Sparsity in Travel Recommendation Agents Using Fuzzy Logic Approach. 2022.
Nilashi, M., et al., Preference learning for eco-friendly hotels recommendation: A multi-criteria collaborative filtering approach. Journal of Cleaner Production, 2019. 215: p. 767-783.
Nilashi, M., O. bin Ibrahim, and N. Ithnin, Hybrid recommendation approaches for multi-criteria collaborative filtering. Expert Systems with Applications, 2014. 41(8): p. 3879-3900.
Nilashi, M., O. bin Ibrahim, and N. Ithnin, Multi-criteria collaborative filtering with high accuracy using higher order singular value decomposition and Neuro-Fuzzy system. Knowledge-Based Systems, 2014. 60: p. 82-101.
Nilashi, M., et al., A multi-criteria collaborative filtering recommender system for the tourism domain using Expectation Maximization (EM) and PCA–ANFIS. Electronic Commerce Research and Applications, 2015. 14(6): p. 542-562.
Nilashi, M., et al., Measuring country sustainability performance using ensembles of neuro-fuzzy technique. Sustainability, 2018. 10(8): p. 2707.
Nilashi, M., et al., A soft computing method for the prediction of energy performance of residential buildings. Measurement, 2017. 109: p. 268-280.
Nilashi, M., et al., A multi-criteria recommendation system using dimensionality reduction and Neuro-Fuzzy techniques. Soft Computing, 2015. 19(11): p. 3173-3207.
Nilashi, M., et al., Analysis of travellers’ online reviews in social networking sites using fuzzy logic approach. International Journal of Fuzzy Systems, 2019. 21(5): p. 1367-1378.
Yadegaridehkordi, E., et al., Predicting determinants of hotel success and development using Structural Equation Modelling (SEM)-ANFIS method. Tourism Management, 2018. 66: p. 364-386.
Zogaan, W.A., et al., A combined method of optimized learning vector quantization and neuro-fuzzy techniques for predicting unified Parkinson's disease rating scale using vocal features. MethodsX, 2024. 12: p. 102553.
Nilashi, M., et al., Coronary heart disease diagnosis through self-organizing map and fuzzy support vector machine with incremental updates. International Journal of Fuzzy Systems, 2020. 22(4): p. 1376-1388.
Nilashi, M., Support Vector Regression for Data-Driven Decision Making. Journal of Soft Computing and Decision Support Systems, 2025. 12(4): p. 1-7.
Feng, K., et al., Myocardial infarction classification based on convolutional neural network and recurrent neural network. Applied Sciences, 2019. 9(9): p. 1879.
Nilashi, M., et al., Early Detection of Diabetic Retinopathy Using Ensemble Learning Approach. Journal of Soft Computing & Decision Support Systems, 2019. 6(2).
Nilashi, M., et al., Electroencephalography (EEG) eye state classification using learning vector quantization and bagged trees. Heliyon, 2023. 9(4).
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