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Voice signal processing is crucial for understanding speaker identity and detecting certain neurological diseases such as Alzheimer's disease, depression and Parkinson's disease. This book highlights innovative methods based on speech signal processing to distinguish people with Parkinson's disease from healthy individuals. Accurate diagnosis is essential in the medical field, as it represents a crucial step towards recovery. To achieve this accuracy, the book is based on understanding the properties of the speech signal, extracting its features and analyzing them from multiple voice samples. A vector of features is then tested by a supervised classifier, selecting the most significant features to improve the Parkinson's disease diagnostic system. The results show that this method is effective in improving the quality of diagnosis in Parkinson's patients.Keywords: Parkinson's disease, voice signal processing, voice processing, wraparound methods, Machine learning.