Investigating the Reordering Capability in CTC-based Non-Autoregressive End-to-End Speech Translation
Year: 2021

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 gradientbased visualization provides an intuitive way to take a