Abstract / truncated to 115 words (read the full abstract)

Voice pathology is a diverse field that includes various disorders affecting vocal quality and production. Using audio machine learning for voice pathology classification represents an innovative approach to diagnosing a wide range of voice disorders. Despite extensive research in this area, there remains a significant gap in the development of classifiers and their ability to adapt and generalize effectively. This thesis aims to address this gap by contributing new insights and methods. This research provides a comprehensive exploration of automatic voice pathology classification, focusing on challenges such as data limitations and the potential of integrating multiple modalities to enhance diagnostic accuracy and adaptability. To achieve generalization capabilities and enhance the flexibility of the classifier across ... toggle 18 keywords

deep learning – neural networks – voice pathology classification – covid-19 – convolutional neural networks – machine learning – FEMH – SVD – coswara – virufy – respriratory sounds – phonotrauma – dysphonia – neoplasm – electroglottographic data – attention mechanisms – fully convolutional neural networks – multimodal networks

Follow @eurasip