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The RWTH Aachen Machine Translation System for IWSLT 2016.

, , , , and . IWSLT, International Workshop on Spoken Language Translation, (2016)

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On the Relevance of Phoneme Duration Variability of Synthesized Training Data for Automatic Speech Recognition., , and . ASRU, page 1-8. IEEE, (2023)Generating Synthetic Audio Data for Attention-Based Speech Recognition Systems., , , and . CoRR, (2019)Automatic Video Dubbing at AppTek., , , , , , and . EAMT, page 349-350. European Association for Machine Translation, (2022)Generating Synthetic Audio Data for Attention-Based Speech Recognition Systems., , , and . ICASSP, page 7069-7073. IEEE, (2020)Comparing the Benefit of Synthetic Training Data for Various Automatic Speech Recognition Architectures., , , , and . ASRU, page 788-795. IEEE, (2021)Learning Bilingual Sentence Embeddings via Autoencoding and Computing Similarities with a Multilayer Perceptron., , , , , and . RepL4NLP@ACL, page 61-71. Association for Computational Linguistics, (2019)The RWTH Aachen Machine Translation Systems for IWSLT 2017., , , and . IWSLT, page 29-34. International Workshop on Spoken Language Translation, (2017)The RWTH Aachen University Filtering System for the WMT 2018 Parallel Corpus Filtering Task., , , , , and . WMT (shared task), page 946-954. Association for Computational Linguistics, (2018)The RWTH Aachen University English-German and German-English Machine Translation System for WMT 2017., , , , , , , and . WMT, page 358-365. Association for Computational Linguistics, (2017)Take the Hint: Improving Arabic Diacritization with Partially-Diacritized Text., , , and . INTERSPEECH, page 3949-3953. ISCA, (2023)