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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)Adaptation and Discriminative Training of Acoustic Models., and . Techniques for Noise Robustness in Automatic Speech Recognition, Wiley, (2012)Recent Advances in End-to-End Spoken Language Understanding., , , , and . SLSP, volume 11816 of Lecture Notes in Computer Science, page 44-55. Springer, (2019)On the use of Self-supervised Pre-trained Acoustic and Linguistic Features for Continuous Speech Emotion Recognition., , , and . CoRR, (2020)Dynamic selection of language models in a dialogue system., , and . INTERSPEECH, page 214-217. ISCA, (2000)End2End Acoustic to Semantic Transduction., , , , , , and . ICASSP, page 7448-7452. IEEE, (2021)Findings of the IWSLT 2022 Evaluation Campaign., , , , , , , , , and 33 other author(s). IWSLT@ACL, page 98-157. Association for Computational Linguistics, (2022)Automatic indexing of speech segments with spontaneity levels on large audio database., , and . SSCS@MM, page 39-44. ACM, (2010)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)Is one brick enough to break the wall of spoken dialogue state tracking?, , and . CoRR, (2023)