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On the use of Self-supervised Pre-trained Acoustic and Linguistic Features for Continuous Speech Emotion Recognition., , , and . CoRR, (2020)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)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 linguistic consistency in systems for human-computer dialogues., , , and . IEEE Trans. Speech Audio Process., 11 (6): 746-756 (2003)Adaptation and Discriminative Training of Acoustic Models., and . Techniques for Noise Robustness in Automatic Speech Recognition, Wiley, (2012)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)The LIUM speech transcription system: a CMU Sphinx III-based system for French broadcast news., , , and . INTERSPEECH, page 1653-1656. ISCA, (2005)Investigating Self-Supervised Pre-Training for End-to-End Speech Translation., , , , and . INTERSPEECH, page 1466-1470. ISCA, (2020)Dynamic selection of language models in a dialogue system., , and . INTERSPEECH, page 214-217. ISCA, (2000)Speaker Adaptive Training and Mixup Regularization for Neural Network Acoustic Models in Automatic Speech Recognition., , and . INTERSPEECH, page 2414-2418. ISCA, (2018)