Papers by Mathieu Balaguer
Exploring Pathological Speech Quality Assessment with ASR-Powered Wav2Vec2 in Data-Scarce Context (2024.lrec-main)
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| Challenge: | Current studies only gain good results on simple tasks such as binary classification due to data scarcity. |
| Approach: | They propose to use the pre-trained Wav2Vec2 architecture for both SSL, and ASR as feature extractor in speech assessment. |
| Outcome: | The proposed system achieves the best results on the HNC dataset using 95 training samples. |
Interpretable Assessment of Speech Intelligibility Using Deep Learning: A Case Study on Speech Disorders Due to Head and Neck Cancers (2024.lrec-main)
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Sondes Abderrazek, Corinne Fredouille, Alain Ghio, Muriel Lalain, Christine Meunier, Mathieu Balaguer, Virginie Woisard
| Challenge: | Using deep learning, speech disorders can be evaluated by perceptual measures, but they are subject to subjectivity and lack of reproducibility. |
| Approach: | They propose to use deep-learning to explain hidden representations in a deep- learning speech model to provide a deeper understanding of the final intelligibility assessment of patients with Head and Neck Cancers. |
| Outcome: | The proposed approach predicts speech intelligibility and severity of patients with Head and Neck Cancers while giving relevant interpretations of the final assessment at the phonemes and phonetic feature levels. |
Carcinologic Speech Severity Index Project: A Database of Speech Disorder Productions to Assess Quality of Life Related to Speech After Cancer (L18-1)
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Corine Astésano, Mathieu Balaguer, Jérôme Farinas, Corinne Fredouille, Pascal Gaillard, Alain Ghio, Imed Laaridh, Muriel Lalain, Benoît Lepage, Julie Mauclair, Olivier Nocaudie, Julien Pinquier, Oriol Pont, Gilles Pouchoulin, Michèle Puech, Danièle Robert, Etienne Sicard, Virginie Woisard
| Challenge: | Increasing mortality in cancerology highlights the importance of reducing the impact on the Quality of Life after cancer treatment. |
| Approach: | They collect a large database of french speech recordings aimed at validating Disorder Severity Indexes. |
| Outcome: | The collected data will be available to the scientific community through the GIS Parolotheque. |