Benedetta Perrone
Voice Classification in Parkinson’s Disease Using Transformer Models and Error Rate Metrics.
Rel. Gabriella Olmo, Federica Amato. Politecnico di Torino, Corso di laurea magistrale in Ingegneria Biomedica, 2024
|
Preview |
PDF (Tesi_di_laurea)
- Tesi
Licenza: Creative Commons Attribution Non-commercial No Derivatives. Download (4MB) | Preview |
Abstract
Parkinson's disease (PD) is a neurodegenerative disorder resulting from the progressive degeneration of dopaminergic neurons in the substantia nigra pars compacta. Alongside motor symptoms like bradykinesia, tremor, and rigidity, PD is also associated with non-motor impairments, including cognitive decline, depression, sleep disorders, and autonomic dysfunctions. One of the most prevalent non-motor symptoms is the alteration of voice and speech, affecting up to 90% of PD patients. The progressive decline in vocal function can lead to hypokinetic dysarthria, reducing speech intelligibility, volume, and prosody, with a significant impact on patients’ quality of life. This study has two main objectives: (1) to distinguish between healthy individuals and those with Parkinson's disease based on vocal characteristics, and (2) to assess disease severity using Word Error Rate (WER) and Character Error Rate (CER), exploring their correlation with Unified Parkinson's Disease Rating Scale (UPDRS) scores.
The models used include the Vision Transformer (ViT) and the Audio Spectrogram Transformer (AST), trained on vocal recordings from datasets comprising both PD patients and healthy controls
Relatori
Anno Accademico
Tipo di pubblicazione
Numero di pagine
Corso di laurea
Classe di laurea
URI
![]() |
Modifica (riservato agli operatori) |
