A Few-Shot Approach to Dysarthric Speech Intelligibility Level
  Classification Using Transformers
Paleti Nikhil Chowdary Vadlapudi Sai Aravind Gorantla V N S L Vishnu Vardhan Menta Sai Akshay Menta Sai Aashish Jyothish Lal. G
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%.