@inproceedings{abderrazek-etal-2024-interpretable,
    title = "Interpretable Assessment of Speech Intelligibility Using Deep Learning: A Case Study on Speech Disorders Due to Head and Neck Cancers",
    author = "Abderrazek, Sondes  and
      Fredouille, Corinne  and
      Ghio, Alain  and
      Lalain, Muriel  and
      Meunier, Christine  and
      Balaguer, Mathieu  and
      Woisard, Virginie",
    editor = "Calzolari, Nicoletta  and
      Kan, Min-Yen  and
      Hoste, Veronique  and
      Lenci, Alessandro  and
      Sakti, Sakriani  and
      Xue, Nianwen",
    booktitle = "Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)",
    month = may,
    year = "2024",
    address = "Torino, Italia",
    publisher = "ELRA and ICCL",
    url = "https://aclanthology.org/2024.lrec-main.803",
    pages = "9170--9179",
    abstract = "This paper sheds light on a relatively unexplored area which is deep learning interpretability for speech disorder assessment and characterization. Building upon a state-of-the-art methodology for the explainability and interpretability of hidden representation inside a deep-learning speech model, we provide a deeper understanding and interpretation of the final intelligibility assessment of patients experiencing speech disorders due to Head and Neck Cancers (HNC). Promising results have been obtained regarding the prediction of speech intelligibility and severity of HNC patients while giving relevant interpretations of the final assessment both at the phonemes and phonetic feature levels. The potential of this approach becomes evident as clinicians can acquire more valuable insights for speech therapy. Indeed, this can help identify the specific linguistic units that affect intelligibility from an acoustic point of view and enable the development of tailored rehabilitation protocols to improve the patient{'}s ability to communicate effectively, and thus, the patient{'}s quality of life.",
}

This paper sheds light on a relatively unexplored area which is deep learning interpretability for speech disorder assessment and characterization. Building upon a state-of-the-art methodology for the explainability and interpretability of hidden representation inside a deep-learning speech model, we provide a deeper understanding and interpretation of the final intelligibility assessment of patients experiencing speech disorders due to Head and Neck Cancers (HNC). Promising results have been obtained regarding the prediction of speech intelligibility and severity of HNC patients while giving relevant interpretations of the final assessment both at the phonemes and phonetic feature levels. The potential of this approach becomes evident as clinicians can acquire more valuable insights for speech therapy. Indeed, this can help identify the specific linguistic units that affect intelligibility from an acoustic point of view and enable the development of tailored rehabilitation protocols to improve the patient{'}s ability to communicate effectively, and thus, the patient{'}s quality of life.
Abderrazek, Sondes Fredouille, Corinne Ghio, Alain Lalain, Muriel Meunier, Christine Balaguer, Mathieu Woisard, Virginie
Interpretable Assessment of Speech Intelligibility Using Deep Learning: A Case Study on Speech Disorders Due to Head and Neck Cancers