A Few-Shot Approach to Dysarthric Speech Intelligibility Level Classification Using Transformers
This work addresses the problem of limited data for dysarthria classification, which is important for developing treatment plans and improving communication for individuals with speech disorders, though it is incremental in nature.
The paper tackled dysarthric speech intelligibility level classification using a few-shot transformer approach, achieving 85% accuracy on a subset of the UASpeech dataset for medium intelligibility patients and 67% accuracy for multiclass classification.
Dysarthria is a speech disorder that hinders communication due to difficulties in articulating words. Detection of dysarthria is important for several reasons as it can be used to develop a treatment plan and help improve a person's quality of life and ability to communicate effectively. Much of the literature focused on improving ASR systems for dysarthric speech. The objective of the current work is to develop models that can accurately classify the presence of dysarthria and also give information about the intelligibility level using limited data by employing a few-shot approach using a transformer model. This work also aims to tackle the data leakage that is present in previous studies. Our whisper-large-v2 transformer model trained on a subset of the UASpeech dataset containing medium intelligibility level patients achieved an accuracy of 85%, precision of 0.92, recall of 0.8 F1-score of 0.85, and specificity of 0.91. Experimental results also demonstrate that the model trained using the 'words' dataset performed better compared to the model trained on the 'letters' and 'digits' dataset. Moreover, the multiclass model achieved an accuracy of 67%.