Exploring the Impact of Fine-Tuning the Wav2vec2 Model in Database-Independent Detection of Dysarthric Speech-Reference-Cited by-同舟云学术

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Exploring the Impact of Fine-Tuning the Wav2vec2 Model in Database-Independent Detection of Dysarthric Speech

Published:2024-08 Issue:8 Volume:28 Page:4951-4962
ISSN:2168-2194
Container-title:IEEE Journal of Biomedical and Health Informatics
language:
Short-container-title:IEEE J. Biomed. Health Inform.

Author:

Javanmardi Farhad¹^ORCID,Kadiri Sudarsana Reddy¹^ORCID,Alku Paavo¹^ORCID

Affiliation:

1. Department of Information and Communications Engineering, Aalto University, Espoo, Finland

Funder

Academy of Finland

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Link

http://xplorestaging.ieee.org/ielx7/6221020/10625760/10508947.pdf?arnumber=10508947

Reference55 articles.

1. Articulatory Movements During Vowels in Speakers With Dysarthria and Healthy Controls

2. A comparison of data augmentation methods in voice pathology detection;Javanmardi;Comput. Speech Lang.,2024

3. Simulating Dysarthric Speech for Training Data Augmentation in Clinical Speech Applications

4. Voice-Specific Augmentations for Parkinson’s Disease Detection Using Deep Convolutional Neural Network

Cited by 1 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Analyzing Wav2Vec 1.0 Embeddings for Cross-Database Parkinson’s Disease Detection and Speech Features Extraction;Sensors;2024-08-26

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