@inproceedings{b-etal-2024-severity,
    title = "Severity Classification and Dysarthric Speech Detection using Self-Supervised Representations",
    author = "Sanjay, B  and
      M.K, Priyadharshini  and
      P, Vijayalakshmi  and
      T, Nagarajan",
    editor = "Lalitha Devi, Sobha  and
      Arora, Karunesh",
    booktitle = "Proceedings of the 21st International Conference on Natural Language Processing (ICON)",
    month = dec,
    year = "2024",
    address = "AU-KBC Research Centre, Chennai, India",
    publisher = "NLP Association of India (NLPAI)",
    url = "https://preview.aclanthology.org/jlcl-multiple-ingestion/2024.icon-1.74/",
    pages = "621--628",
    abstract = "Automatic detection and classification of dysarthria severity from speech provides a non-invasive and efficient diagnostic tool, offering clinicians valuable insights to guide treatment and therapy decisions. Our study evaluated two pre-trained models{---}wav2vec2-BASE and distilALHuBERT, for feature extraction to build speech detection and severity-level classification systems for dysarthric speech. We conducted experiments on the TDSC dataset using two approaches: a machine learning model (support vector machine, SVM) and a deep learning model (convolutional neural network, CNN). Our findings showed that features derived from distilALHuBERT significantly outperformed those from wav2vec2-BASE in both dysarthric speech detection and severity classification tasks. Notably, the distilALHuBERT features achieved 99{\%} accuracy in automatic detection and 95{\%} accuracy in severity classification, surpassing the performance of wav2vec2 features."
}