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The Spoken Language Understanding MEDIA Benchmark Dataset in the Era of Deep Learning: data updates, training and evaluation tools.

, , , , , , , and . LREC, page 1595-1602. European Language Resources Association, (2022)

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Adaptation and Discriminative Training of Acoustic Models., and . Techniques for Noise Robustness in Automatic Speech Recognition, Wiley, (2012)Speaker Adaptive Training and Mixup Regularization for Neural Network Acoustic Models in Automatic Speech Recognition., , and . INTERSPEECH, page 2414-2418. ISCA, (2018)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)LeBenchmark 2.0: a Standardized, Replicable and Enhanced Framework for Self-supervised Representations of French Speech., , , , , , , , , and 12 other author(s). CoRR, (2023)Is one brick enough to break the wall of spoken dialogue state tracking?, , and . CoRR, (2023)Acoustic and linguistic representations for speech continuous emotion recognition in call center conversations., , , and . CoRR, (2023)Some voices are too common: Building fair speech recognition systems using the Common Voice dataset., and . CoRR, (2023)Recent Advances in End-to-End Spoken Language Understanding., , , , and . SLSP, volume 11816 of Lecture Notes in Computer Science, page 44-55. Springer, (2019)