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Investigation of Large-Margin Softmax in Neural Language Modeling.

, , , , and . INTERSPEECH, page 3645-3649. ISCA, (2020)

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RWTH ASR Systems for LibriSpeech: Hybrid vs Attention - w/o Data Augmentation., , , , , , , and . CoRR, (2019)End-To-End Training of a Neural HMM with Label and Transition Probabilities., , , , and . ASRU, page 1-8. IEEE, (2023)Efficient Utilization of Large Pre-Trained Models for Low Resource ASR., , , , and . ICASSP Workshops, page 1-5. IEEE, (2023)End-to-End Speech Recognition: A Survey., , , , and . IEEE ACM Trans. Audio Speech Lang. Process., (2024)Frame-Level MMI as A Sequence Discriminative Training Criterion for LVCSR., , and . ICASSP, page 6904-6908. IEEE, (2020)An Analysis of Local Monotonic Attention Variants., , , and . INTERSPEECH, page 1398-1402. ISCA, (2019)Comparison of Lattice-Free and Lattice-Based Sequence Discriminative Training Criteria for LVCSR., , and . INTERSPEECH, page 1601-1605. ISCA, (2019)The Conformer Encoder May Reverse the Time Dimension., , , , and . CoRR, (2024)The Impact of ASR on the Automatic Analysis of Linguistic Complexity and Sophistication in Spontaneous L2 Speech., , , and . Interspeech, page 4453-4457. ISCA, (2021)On the Relevance of Phoneme Duration Variability of Synthesized Training Data for Automatic Speech Recognition., , and . ASRU, page 1-8. IEEE, (2023)