This paper describes the ESPnet-ST group's IWSLT 2021 submission in the
offline speech translation track. This year we made various efforts on training
data, architecture, and audio segmentation. On the data side, we investigated
sequence-level knowledge distillation (SeqKD) for end-to-end (E2E) speech
translation. Specifically, we used multi-referenced SeqKD from multiple
teachers trained on different amounts of bitext. On the architecture side, we
adopted the Conformer encoder and the Multi-Decoder architecture, which equips
dedicated decoders for speech recognition and translation tasks in a unified
encoder-decoder model and enables search in both source and target language
spaces during inference. We also significantly improved audio segmentation by
using the pyannote.audio toolkit and merging multiple short segments for long
context modeling. Experimental evaluations showed that each of them contributed
to large improvements in translation performance. Our best E2E system combined
all the above techniques with model ensembling and achieved 31.4 BLEU on the
2-ref of tst2021 and 21.2 BLEU and 19.3 BLEU on the two single references of
tst2021.

本研究描述了 ESPnet-ST 组在 IWSLT 2021 中提交的语音翻译模型。通过训练数据、模型架构和音频分割等方面的努力，结合多种技术如序列级知识蒸馏、Conformer 编码器等，同时使用模型组合达到了 31.4 BLEU 的最佳翻译性能。