Automated Detection and Severity Assessment of Dysarthria using Raw Speech
Author:
Affiliation:
1. VIT-AP University,School of Electronics Engineering,Amaravati,Andhra Pradesh,India,522237
Publisher
IEEE
Link
http://xplorestaging.ieee.org/ielx7/10306338/10306339/10307923.pdf?arnumber=10307923
Reference24 articles.
1. On using the UA-Speech and TORGO databases to validate automatic dysarthric speech classification approaches;schu,2022
2. Automatic Assessment of Sentence-Level Dysarthria Intelligibility Using BLSTM
3. The TORGO database of acoustic and articulatory speech from speakers with dysarthria
4. Automatic assessment of dysarthria severity level using audio descriptors
5. Automated Dysarthria Severity Classification: A Study on Acoustic Features and Deep Learning Techniques
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1. Automated ASD detection in children from raw speech using customized STFT-CNN model;International Journal of Speech Technology;2024-07-26
2. Harnessing Deep Learning Techniques for Dysarthria Detection;2024 Third International Conference on Intelligent Techniques in Control, Optimization and Signal Processing (INCOS);2024-03-14
3. Dysarthria Diagnosis and Dysarthric Speaker Identification Using Raw Speech Model;2024 National Conference on Communications (NCC);2024-02-28
4. Variable STFT Layered CNN Model for Automated Dysarthria Detection and Severity Assessment Using Raw Speech;Circuits, Systems, and Signal Processing;2024-02-22
5. Detecting Autism Spectrum Disorder from Raw Speech in Children using STFT Layered CNN Model;2024 International Conference on Green Energy, Computing and Sustainable Technology (GECOST);2024-01-17
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