Exploring Self-Supervised Pre-Trained ASR Models for Dysarthric and Elderly Speech Recognition

Author:

Hu Shujie1,Xie Xurong2,Jin Zengrui1,Geng Mengzhe1,Wang Yi1,Cui Mingyu1,Deng Jiajun1,Liu Xunying1,Meng Helen1

Affiliation:

1. The Chinese University of Hong Kong,Hong Kong SAR,China

2. Chinese Academy of Sciences,Institute of Software,China

Funder

National Natural Science Foundation of China

Publisher

IEEE

Reference44 articles.

1. Personalized Adversarial Data Augmentation for Dysarthric and Elderly Speech Recognition;jin,2022

2. Exploiting Cross-domain And Cross-Lingual Ultrasound Tongue Imaging Features For Elderly And Dysarthric Speech Recognition;hu,2022

3. Dementia detection using automatic analysis of conversations

4. Revisiting hybrid and GMM-HMM system combination techniques

5. Improved ASR Performance for Dysarthric Speech Using Two-stage DataAugmentation

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1. Exploring the Impact of Fine-Tuning the Wav2vec2 Model in Database-Independent Detection of Dysarthric Speech;IEEE Journal of Biomedical and Health Informatics;2024-08

2. Enhancing Pre-Trained ASR System Fine-Tuning for Dysarthric Speech Recognition Using Adversarial Data Augmentation;ICASSP 2024 - 2024 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP);2024-04-14

3. UNIT-DSR: Dysarthric Speech Reconstruction System Using Speech Unit Normalization;ICASSP 2024 - 2024 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP);2024-04-14

4. A Voice User Interface on the Edge for People with Speech Impairments;Electronics;2024-04-07

5. Toward Stealthy Backdoor Attacks Against Speech Recognition via Elements of Sound;IEEE Transactions on Information Forensics and Security;2024

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