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%.

翻译：构音障碍是一种因发音困难而影响交流的言语障碍。检测构音障碍具有重要意义,可用于制定治疗方案、改善患者生活质量及有效沟通能力。现有文献多集中于改进构音障碍语音的ASR系统。本研究旨在开发能准确分类构音障碍存在与否,并通过采用基于Transformer模型的少样本方法利用有限数据提供清晰度等级信息的模型。同时,本研究致力于解决既往研究中存在的数据泄露问题。我们的whisper-large-v2 Transformer模型在包含中清晰度等级患者的UASpeech子集上进行训练后,达到了85%的准确率、0.92的精确率、0.8的召回率、0.85的F1分数及0.91的特异性。实验结果表明,使用"单词"数据集训练的模型性能优于使用"字母"和"数字"数据集训练的模型。此外,多分类模型达到了67%的准确率。