End-to-end (E2E) speech-to-text translation (ST) often depends on pretraining
its encoder and/or decoder using source transcripts via speech recognition or
text translation tasks, without which translation performance drops
substantially. However, transcripts are not always available, and how
significant such pretraining is for E2E ST has rarely been studied in the
literature. In this paper, we revisit this question and explore the extent to
which the quality of E2E ST trained on speech-translation pairs alone can be
improved. We reexamine several techniques proven beneficial to ST previously,
and offer a set of best practices that biases a Transformer-based E2E ST system
toward training from scratch. Besides, we propose parameterized distance
penalty to facilitate the modeling of locality in the self-attention model for
speech. On four benchmarks covering 23 languages, our experiments show that,
without using any transcripts or pretraining, the proposed system reaches and
even outperforms previous studies adopting pretraining, although the gap
remains in (extremely) low-resource settings. Finally, we discuss neural
acoustic feature modeling, where a neural model is designed to extract acoustic
features from raw speech signals directly, with the goal to simplify inductive
biases and add freedom to the model in describing speech. For the first time,
we demonstrate its feasibility and show encouraging results on ST tasks.

本文通过重新审视机器翻译中的 ST 和文本翻译任务技术，提出了一套训练 E2E ST 系统的最佳实践，其中包括参数化距离惩罚、模拟本地化技术等。实验表明，在不使用转录文本和预训练的情况下，该系统可以达到并甚至超过之前使用预训练方法的研究水平，对于极低资源环境依然有差距。此外，我们还首次证明了神经声学特征建模的可行性，并在 ST 任务中取得了令人鼓舞的效果。