  Combining end-to-end speech translation (ST) and non-autoregressive (NAR)
generation is promising in language and speech processing for their advantages
of less error propagation and low latency. In this paper, we investigate the
potential of connectionist temporal classification (CTC) for non-autoregressive
speech translation (NAST). In particular, we develop a model consisting of two
encoders that are guided by CTC to predict the source and target texts,
respectively. Introducing CTC into NAST on both language sides has obvious
challenges: 1) the conditional independent generation somewhat breaks the
interdependency among tokens, and 2) the monotonic alignment assumption in
standard CTC does not hold in translation tasks. In response, we develop a
prediction-aware encoding approach and a cross-layer attention approach to
address these issues. We also use curriculum learning to improve convergence of
training. Experiments on the MuST-C ST benchmarks show that our NAST model
achieves an average BLEU score of 29.5 with a speed-up of 5.67$\times$, which
is comparable to the autoregressive counterpart and even outperforms the
previous best result of 0.9 BLEU points.
