  Speech translation (ST) systems translate speech in one language to text in
another language. End-to-end ST systems (e2e-ST) have gained popularity over
cascade systems because of their enhanced performance due to reduced latency
and computational cost. Though resource intensive, e2e-ST systems have the
inherent ability to retain para and non-linguistic characteristics of the
speech unlike cascade systems. In this paper, we propose to use an e2e
architecture for English-Hindi (en-hi) ST. We use two imperfect machine
translation (MT) services to translate Libri-trans en text into hi text. While
each service gives MT data individually to generate parallel ST data, we
propose a data augmentation strategy of noisy MT data to aid robust ST. The
main contribution of this paper is the proposal of a data augmentation
strategy. We show that this results in better ST (BLEU score) compared to brute
force augmentation of MT data. We observed an absolute improvement of 1.59 BLEU
score with our approach.
