Publisher's Synopsis
This book explores the use of speech and language analysis for evaluating and monitoring Major Depression Disorder (MDD), Alzheimer's Disease (AD), and Parkinson's Disease (PD). By combining acoustic and linguistic features with machine learning, it addresses challenges in diagnosis and symptom overlap while aiming to improve therapy outcomes and patient monitoring. For MDD, the study analyzes therapy effectiveness by evaluating speech descriptors' impact on therapy, changes in emotional and speech patterns, and neural embeddings' suitability for tracking depression levels using contrastive learning. In AD, it applies automatic speech analysis to classify the disease, predict cognitive states, and detect pre-clinical stages. This includes AD classification using acoustic, emotional, and linguistic features; cognitive state prediction aligned with clinical assessments; and detection of pre-clinical stages linked to the PSEN1 mutation. For PD, speech analysis focuses on classifying and predicting neurological and motor states, incorporating spectral-based representation learning for disease severity prediction and identifying depression through emotional speech analysis. The book also examines biases in data collection and emphasizes the need for robust, multilingual models to enable cross-language feature transferability. Findings demonstrate the potential of speech and language analysis to support diagnosis and monitor treatment across neurological and psychiatric disorders.
