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%.

通过使用具有有限数据的 Transformer 模型，本研究旨在准确分类失语和提供有关可理解性水平的信息，同时解决以往研究中存在的数据泄漏问题，并表明所采用的多课程模型在特定数据集上获得了 67% 的准确度。