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- W4312944707 abstract "End-to-end models have significant potential in most languages and recently proved the robustness in ASR tasks. Many robust architectures are proposed, and among many techniques, Recurrent Neural Network - Transducer (RNN-T) shows remarkable success. However, with background noise or reverb in spontaneous speech, this architecture generally suffers from high deletion error problems. For this reason, we propose the blank label re-weighting technique to improve the state-of-the-art Conformer transducer model. Our proposed system adopts the Stochastic Weight Averaging approach, stabilizing the training process. Our work achieved the first rank with a 4.17% of word error rate in Task 2 of the VLSP 2021 Competition.
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- W4312944707 date "2022-06-30" @default.
- W4312944707 modified "2023-10-14" @default.
- W4312944707 title "ASR - VLSP 2021: Automatic Speech Recognition with Blank Label Re-weighting" @default.
- W4312944707 doi "https://doi.org/10.25073/2588-1086/vnucsce.321" @default.
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