Dysarthric Speech Recognition Using Pseudo-Labeling, Self-Supervised Feature Learning, and a Joint Multi-Task Learning Approach
Journal: IEEE Access
Year: 2024

In this paper, we investigate the use of the spontaneous speech of dysarthric people for training an automatic speech recognition (ASR) model for them. Although the spontaneous speech of dysarthric people can be collected relatively easily compared to script-reading speech, which is obtained by having them read a prepared script, labeling the spontaneous speech of dysarthric people is very difficult and costly. For training an ASR model using unlabeled speech data, pseudo-labeling and self-super