We study the possibilities of building a non-autoregressive speech-to-text
translation model using connectionist temporal classification (CTC), and use
CTC-based automatic speech recognition as an auxiliary task to improve the
performance. CTC's success on translation is counter-intuitive due to its
monotonicity assumption, so we analyze its reordering capability. Kendall's tau
distance is introduced as the quantitative metric, and gradient-based
visualization provides an intuitive way to take a closer look into the model.
Our analysis shows that transformer encoders have the ability to change the
word order and points out the future research direction that worth being
explored more on non-autoregressive speech translation.

使用连接主义时间分类（CTC）构建非自回归语音到文本翻译模型，并将基于 CTC 的自动语音识别作为辅助任务来提高性能。通过对 CTC 的重新排序能力进行分析，使用肯德尔 - 塔距离作为定量指标并提供梯度可视化，进一步探索了非自回归语音翻译的研究方向。