SAMI: an M-Health application to telemonitor intelligibility and speech disorder severity in head and neck cancers

• Published in: Frontiers in Artificial Intelligence
• Main Authors: Quintas, Sebastião, Vaysse, Robin, Balaguer, Mathieu, Roger, Vincent, Mauclair, Julie, Farinas, Jérôme, Woisard, Virginie, Pinquier, Julien
• Other Authors: Institut de recherche en informatique de Toulouse (IRIT), Université Toulouse Capitole (UT Capitole), Université de Toulouse (UT)-Université de Toulouse (UT)-Université Toulouse - Jean Jaurès (UT2J), Université de Toulouse (UT)-Université Toulouse III - Paul Sabatier (UT3), Université de Toulouse (UT)-Centre National de la Recherche Scientifique (CNRS)-Institut National Polytechnique (Toulouse) (Toulouse INP), Université de Toulouse (UT)-Toulouse Mind & Brain Institut (TMBI), Université Toulouse - Jean Jaurès (UT2J), Université de Toulouse (UT)-Université de Toulouse (UT)-Université Toulouse III - Paul Sabatier (UT3), Université de Toulouse (UT), Service Oto-Rhino-Laryngologie (ORL) et chirurgie cervico-faciale CHU Toulouse, Pôle Clinique des Voies respiratoires CHU Toulouse, Centre Hospitalier Universitaire de Toulouse (CHU Toulouse)-Centre Hospitalier Universitaire de Toulouse (CHU Toulouse)
• Format: Article in Journal/Newspaper
• Language: English
• Published: HAL CCSD 2024
• Subjects: speech intelligibility; speaker embeddings; head and neck cancer; deep learning; healthcare application; [INFO]Computer Science [cs]; sami
• Online Access: https://hal.science/hal-04595439; https://doi.org/10.3389/frai.2024.1359094

International audience Perceptual measures, such as intelligibility and speech disorder severity, are widely used in the clinical assessment of speech disorders in patients treated for oral or oropharyngeal cancer. Despite their widespread usage, these measures are known to be subjective and hard to reproduce. Therefore, an M-Health assessment based on an automatic prediction has been seen as a more robust and reliable alternative. Despite recent progress, these automatic approaches still remain somewhat theoretical, and a need to implement them in real clinical practice rises. Hence, in the present work we introduce SAMI, a clinical mobile application used to predict speech intelligibility and disorder severity as well as to monitor patient progress on these measures over time. The first part of this work illustrates the design and development of the systems supported by SAMI. Here, we show how deep neural speaker embeddings are used to automatically regress speech disorder measurements (intelligibility and severity), as well as the training and validation of the system on a French corpus of head and neck cancer. Furthermore, we also test our model on a secondary corpus recorded in real clinical conditions. The second part details the results obtained from the deployment of our system in a real clinical environment, over the course of several weeks. In this section, the results obtained with SAMI are compared to an a posteriori perceptual evaluation, conducted by a set of experts on the new recorded data. The comparison suggests a high correlation and a low error between the perceptual and automatic evaluations, validating the clinical usage of the proposed application.