Automatic Voice Disorder Detection Using Self-Supervised Representations

Automatic Voice Disorder Detection Using Self-Supervised Representations

Many speech features and models, including Deep Neural Networks (DNN), are used for classification tasks between healthy and pathological speech with the Saarbruecken Voice Database (SVD). However, accuracy values of 80.71% for phrases or 82.8% for vowels /aiu/ are the highest...

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Bibliographic Details
Main Authors: Dayana Ribas, Miguel A. Pastor, Antonio Miguel, David Martinez, Alfonso Ortega, Eduardo Lleida
Format: Article
Language:English
Published: IEEE 2023-01-01
Series:IEEE Access
Subjects:
Voice disorder
pathological speech
Saarbruecken voice database
advanced voice function assessment database
self-supervised
class token
Online Access:https://ieeexplore.ieee.org/document/10041907/

Internet

https://ieeexplore.ieee.org/document/10041907/

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