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Multiple-Region Segmentation Without Supervision by Adaptive Global Maximum Clustering., and . IEEE Trans. Image Processing, 21 (4): 1600-1612 (2012)Automatic Severity Classification of Dysarthric Speech by Using Self-Supervised Model with Multi-Task Learning., , , and . ICASSP, page 1-5. IEEE, (2023)Automatic segmentation of leg bones by using active contours., , , and . EMBC, page 4695-4698. IEEE, (2014)Caractérisation des plosives finales dans des langues d'Asie : une étude multilingue du non relâchement (Characterization of Stop Consonants in Asian Languages: A two-language Study of Unreleased)., , , , and . JEP-TALN-RECITAL (1), page 597-605. ATALA et AFCP, (2020)Constructing Korean Learners' L2 Speech Corpus of Seven Languages for Automatic Pronunciation Assessment., , and . LREC/COLING, page 3772-3781. ELRA and ICCL, (2024)Phonology of exceptions for for Korean grapheme-to-phoneme conversion.. INTERSPEECH, page 1285-1289. ISCA, (2004)Dysarthria Detection and Severity Assessment Using Rhythm-Based Metrics., , , and . INTERSPEECH, page 2897-2901. ISCA, (2020)Speech Intelligibility Assessment of Dysarthric Speech by using Goodness of Pronunciation with Uncertainty Quantification., , , and . INTERSPEECH, page 166-170. ISCA, (2023)Automatic Speech Recognition and Assessment Systems Incorporated into Digital Therapeutics for Children with Autism Spectrum Disorder., , , , , , , , and . ICCHP (2), volume 14751 of Lecture Notes in Computer Science, page 328-335. Springer, (2024)A computational method of identifying false positives in the variant calling., , , and . IEEE Big Data, page 6189-6191. IEEE, (2023)