Pathological Speech

Pathological speech analysis focuses on automatically detecting and classifying voice disorders from speech recordings, aiming to improve diagnostic efficiency and patient care. Current research emphasizes robust feature extraction techniques, often employing deep learning models like transformers, recurrent neural networks (RNNs), and autoencoders, alongside signal processing methods such as empirical mode decomposition and wavelet transforms, to overcome challenges posed by noisy data and variability in speech production. These advancements address limitations of traditional methods and improve the accuracy and generalizability of pathological speech detection across diverse disorders and datasets, potentially leading to more accessible and objective clinical assessments.