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Impact Analysis of the Use of Speech and Language Models Pretrained by Self-Supersivion for Spoken Language Understanding.

, , , , , , , and . LREC, page 2949-2956. European Language Resources Association, (2022)

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On the use of Self-supervised Pre-trained Acoustic and Linguistic Features for Continuous Speech Emotion Recognition., , , and . CoRR, (2020)LeBenchmark 2.0: a Standardized, Replicable and Enhanced Framework for Self-supervised Representations of French Speech., , , , , , , , , and 12 other author(s). CoRR, (2023)Acoustic and linguistic representations for speech continuous emotion recognition in call center conversations., , , and . CoRR, (2023)Adaptation and Discriminative Training of Acoustic Models., and . Techniques for Noise Robustness in Automatic Speech Recognition, Wiley, (2012)On the use of linguistic consistency in systems for human-computer dialogues., , , and . IEEE Trans. Speech Audio Process., 11 (6): 746-756 (2003)Recent Advances in End-to-End Spoken Language Understanding., , , , and . SLSP, volume 11816 of Lecture Notes in Computer Science, page 44-55. Springer, (2019)Error detection of grapheme-to-phoneme conversion in text-to-speech synthesis using speech signal and lexical context., , and . ASRU, page 692-697. IEEE, (2017)Leverage Unlabeled Data for Abstractive Speech Summarization with Self-supervised Learning and Back-Summarization., , , , , and . SPECOM, volume 12335 of Lecture Notes in Computer Science, page 572-580. Springer, (2020)Is one brick enough to break the wall of spoken dialogue state tracking?, , and . CoRR, (2023)End2End Acoustic to Semantic Transduction., , , , , , and . ICASSP, page 7448-7452. IEEE, (2021)