Analysis of Pathological Speech Signals

ABSTRACT : This thesis addresses the automatic analysis of speech disorders resulting from a clinical condition (Parkinson's disease and hearing loss) or the natural aging process. For Parkinson's disease, the progression of speech symptoms is evaluated by considering speech recordings captured in the short-term (4 months) and long-term (5 years). Machine learning methods are used to perform three tasks: (1) automatic classification of patients vs. healthy speakers. (2) regression analysis to predict the dysarthria level and neurological state. (3) speaker embeddings to analyze the progression of the speech symptoms over time. For hearing loss, automatic acoustic analysis is performed to evaluate whether the duration and onset of deafness (before or after speech acquisition) influence the speech production of cochlear implant users. Additionally, articulation, prosody, and phonemic analyses show that cochlear implant users present altered speech production even after hearing rehabilitation.

• Autores: Arias Vergara, Tomás
• Tipo de recurso: Doctoral thesis
• Fecha de publicación: 2022
• Institución: Universidad de Antioquia
• Repositorio: Repositorio UdeA
• Idioma: eng
• OAI Identifier: oai:bibliotecadigital.udea.edu.co:10495/31409
• Acceso en línea: https://hdl.handle.net/10495/31409
• Palabra clave: Deep Learning; Aprendizaje profundo; Parkinson Disease; Enfermedad de parkinson; Speech Recognition Software; Software de Reconocimiento del Habla; Cochlear Implants; Implantes Cocleares; Aging; Envejecimiento; Machine learning; Aprendizaje automático (inteligencia artificial); Speech processing
• Rights: openAccess
• License: http://creativecommons.org/licenses/by/2.5/co/