Automated Dysarthria Severity Classification: A Study on Acoustic Features and Deep Learning Techniques-Reference-Cited by-同舟云学术

Automated Dysarthria Severity Classification: A Study on Acoustic Features and Deep Learning Techniques

Published:2022 Issue: Volume:30 Page:1147-1157
ISSN:1534-4320
Container-title:IEEE Transactions on Neural Systems and Rehabilitation Engineering
language:
Short-container-title:IEEE Trans. Neural Syst. Rehabil. Eng.

Author:

Joshy Amlu Anna¹^ORCID,Rajan Rajeev²

Affiliation:

1. Electronics and Communication Engineering Department, College of Engineering Trivandrum, APJ Abdul Kalam Technological University, Thiruvananthapuram, India

2. Department of Computer Science and Engineering, Speech and Music Technology Laboratory, IIT Madras, Chennai, India

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Biomedical Engineering,General Neuroscience,Internal Medicine,Rehabilitation

Link

http://xplorestaging.ieee.org/ielx7/7333/9695946/09762324.pdf?arnumber=9762324

Reference45 articles.

1. Dysarthria intelligibility assessment in a factor analysis total variability space

2. Vowel Articulation in Parkinson's Disease

3. A novel scheme for speaker recognition using a phonetically-aware deep neural network

4. Analysis and Optimization of Bottleneck Features for Speaker Recognition

5. i-vector based speaker recognition on short utterances

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