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Muriel Lalain

2 accepted papers

2024

Interpretable Assessment of Speech Intelligibility Using Deep Learning: A Case Study on Speech Disorders Due to Head and Neck Cancers

COLING 2024main

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, w…

Cited by 2SourcePDFScholar
2022

Towards Interpreting Deep Learning Models to Understand Loss of Speech Intelligibility in Speech Disorders Step 2: Contribution of the Emergence of Phonetic Traits

ICASSP 2022accepted

Apart from the impressive performance it has achieved in several tasks, one of the most important factors remaining for the continuous progress of deep learning is the increased work related to interpretability, especially in a medical context. In a recent work, we presented competitive performance…

Cited by 0SourceScholar