AL-Lisaniyyat
Volume 30, Numéro 2, Pages 170-182
2024-12-30

Dysarthria Severity Detection Using Recurrent And Convolutional Neural Networks



Authors : Hamza Amina . Addou Djamel .

Abstract

The diagnosis and monitoring of dysarthria, a speech disorder caused by neuro-motor problems that affect articulation, depend on a precise evaluation of its severity. When creating automated systems to identify and categorize dysarthric speech, accurate severity classification is essential. Using neural network models, specifically recurrent neural networks (RNN) and convolutional neural networks (CNN), this paper offers a thorough investigation of how to distinguish dysarthric voices among a collection of normal voice samples and categorize the severity of dysarthria. Among the features used in the study are voice quality, prosodic parameters, formants, Mel frequency cepstral coefficients (MFCC), and spectrograms. Comparing the ability of convolutional networks and reccurents to identify abnormalities in normal data, as well as the hybrid model that combines convolutional and reccurent neural networks (CRNN), is our goal. The Nemours corpus database is used to assess these neural network models' performances. Notably, 99.8% is the highest classification accuracy attained with this corpus.

Keywords

Dysarthria classification ; CNN ; RNN ; CRNN ; acoustic parameters ; automatic speech assessment

Les articles similaires

Predictive Modeling Of Ozone Dosing In Drinking Water Treatment Plant Using Deep Learning Comparative Study Between Deep Neural Networks And Convolutional Neural Networks

Hellal Aouatef .  Djeddou Messaoud .  Loukam Imed .  A. Hameed Ibrahim .  Al Dallal Jehad .  Shawaqfah Moayyad . 
pages 69-83.

Automatic Diagnosis System For Classification, Segmentation, Detection And Tracking Of Skin Lesion Based On Deep Convolutional Neural Networks

Benyahia Samia .  Meftah Boudjelal . 
pages 23-27.

Intrusion Detection Based On Neural Networks Neural Networks In Iot

Chaima Bensaid . 
pages 29-33.

A Cad-like Tool Using Convolutional Neural Networks To Improve Radiologist Screening Of Breast Cancer

Kacher Abdelhafidh . 
pages 22-27.

Multi-task Convolutional Neural Networks For Damage Prediction On Solar Panels

Oualha Chiheb Eddine .  Hadji Mohammed Lakhdar . 
pages 33-37.

Formats de citation