JHU IWSLT 2022 Dialect Speech Translation System Description
Jinyi Yang, Amir Hussein, Matthew Wiesner, Sanjeev P. Khudanpur · 2022
This paper details the Johns Hopkins speech translation (ST) system used in the IWLST2022 dialect speech translation task.Our system uses a cascade of automatic speech recognition (ASR) and machine translation (MT).We use a Conformer model for ASR systems and a Transformer model for machine translation.Surprisingly, we found that while using additional ASR training data resulted in only a negligible change in performance as measured by BLEU or word error rate (WER), aggressive text normalization improved BLEU more significantly.We also describe an approach, similar to back-translation, for improving performance using synthetic dialect source text produced from source sentences in mismatched dialects.