The NYA’s Offline Speech Translation System for IWSLT 2024
Yingxin Zhang, Guodong Ma, Binbin Du · 2024
This paper reports the NYA's submissions to IWSLT 2024 Offline Speech Translation (ST) task on the sub-tasks including English to Chinese, Japanese, and German.In detail, we participate in the unconstrained training track using the cascaded ST structure.For the automatic speech recognition (ASR) model, we use the Whisper large-v3 model.For the neural machine translation (NMT) model, the wider and deeper Transformer is adapted as the backbone model.Furthermore, we use data augmentation technologies to augment training data and data filtering strategies to improve the quality of training data.In addition, we explore many MT technologies such as Back Translation, Forward Translation, R-Drop, and Domain Adaptation.Moreover, our model is a one-to-many ST system that utilizes flags for different tasks.Experimental results on the tst2022 test set demonstrate that our model achieves 36.37,20.92, and 24.28 BLEU in En2Zh, En2Ja, and En2De, respectively.