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Make More of Your Data: Minimal Effort Data Augmentation for Automatic Speech Recognition and Translation., , and . CoRR, (2022)Analyzing the Use of Influence Functions for Instance-Specific Data Filtering in Neural Machine Translation., , and . WMT, page 295-309. Association for Computational Linguistics, (2022)On-the-Fly Aligned Data Augmentation for Sequence-to-Sequence ASR., , , and . Interspeech, page 1299-1303. ISCA, (2021)Cascaded Models With Cyclic Feedback For Direct Speech Translation., , and . CoRR, (2020)Compact Speech Translation Models via Discrete Speech Units Pretraining., , and . CoRR, (2024)Sample, Translate, Recombine: Leveraging Audio Alignments for Data Augmentation in End-to-end Speech Translation., , and . ACL (2), page 245-254. Association for Computational Linguistics, (2022)Prosody in Cascade and Direct Speech-to-Text Translation: a case study on Korean Wh-Phrases., , , and . EACL (Findings), page 674-683. Association for Computational Linguistics, (2024)Interactive-Predictive Neural Machine Translation through Reinforcement and Imitation., , and . MTSummit (1), page 96-106. European Association for Machine Translation, (2019)Make More of Your Data: Minimal Effort Data Augmentation for Automatic Speech Recognition and Translation., , and . ICASSP, page 1-5. IEEE, (2023)Cascaded Models with Cyclic Feedback for Direct Speech Translation., , and . ICASSP, page 7508-7512. IEEE, (2021)