Classifying Speech Disorders Using Voice Signals and Machine Learning

This study explores the application of machine learning (ML) and advanced voice signal analysis to classify speech disorders, including vocal tremors, dysarthria, and stuttering. These disorders pose significant challenges to communication and quality of life, requiring precise and reliable diagnostic tools. To address the inherent challenges of limited data availability in this domain, the study employs synthetic voice data generated using mathematical models to augment real-world recordings. This hybrid approach ensures a more comprehensive dataset, enabling robust training and evaluation of machine learning models.Key machine learning algorithms such as Support Vector Machines (SVMs), Random Forest, and Gradient Boosting are utilized to extract and analyze a wide range of acoustic features from speech samples. These methods are selected for their ability to handle complex, nonlinear patterns and to identify subtle distinctions between normal and disordered speech. By leveraging these techniques, the study investigates their performance in accurately classifying speech disorders, emphasizing their potential in improving diagnostic outcomes.The findings highlight the transformative potential of machine learning in the field of speech pathology. ML models demonstrated notable improvements in the precision, efficiency, and consistency of diagnosing speech disorders compared to traditional methods. This breakthrough not only enhances diagnostic reliability but also equips clinicians with objective tools that reduce subjectivity in assessments. Moreover, the integration of these technologies promotes earlier detection of disorders, enabling timely and tailored therapeutic interventions.By facilitating more accurate and accessible diagnostic practices, this work paves the way for significant advancements in patient care. The use of ML-powered tools can bridge the gap between clinical expertise and technological innovation, empowering healthcare professionals to offer personalized treatment plans. Ultimately, this approach has the potential to improve patient outcomes, reduce the stigma associated with speech disorders, and ensure more equitable access to high-quality care for individuals worldwide.