@inproceedings{abderrazek-etal-2024-interpretable-assessment,
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.",
}
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<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.</abstract>
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%0 Conference Proceedings
%T Interpretable Assessment of Speech Intelligibility Using Deep Learning: A Case Study on Speech Disorders Due to Head and Neck Cancers
%A Abderrazek, Sondes
%A Fredouille, Corinne
%A Ghio, Alain
%A Lalain, Muriel
%A Meunier, Christine
%A Balaguer, Mathieu
%A Woisard, Virginie
%Y Calzolari, Nicoletta
%Y Kan, Min-Yen
%Y Hoste, Veronique
%Y Lenci, Alessandro
%Y Sakti, Sakriani
%Y Xue, Nianwen
%S Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)
%D 2024
%8 May
%I ELRA and ICCL
%C Torino, Italia
%F abderrazek-etal-2024-interpretable-assessment
%X 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.
%U https://aclanthology.org/2024.lrec-main.803
%P 9170-9179
Markdown (Informal)
[Interpretable Assessment of Speech Intelligibility Using Deep Learning: A Case Study on Speech Disorders Due to Head and Neck Cancers](https://aclanthology.org/2024.lrec-main.803) (Abderrazek et al., LREC-COLING 2024)
ACL
- Sondes Abderrazek, Corinne Fredouille, Alain Ghio, Muriel Lalain, Christine Meunier, Mathieu Balaguer, and Virginie Woisard. 2024. Interpretable Assessment of Speech Intelligibility Using Deep Learning: A Case Study on Speech Disorders Due to Head and Neck Cancers. In Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024), pages 9170–9179, Torino, Italia. ELRA and ICCL.