  Previous work on end-to-end translation from speech has primarily used
frame-level features as speech representations, which creates longer, sparser
sequences than text. We show that a naive method to create compressed
phoneme-like speech representations is far more effective and efficient for
translation than traditional frame-level speech features. Specifically, we
generate phoneme labels for speech frames and average consecutive frames with
the same label to create shorter, higher-level source sequences for
translation. We see improvements of up to 5 BLEU on both our high and low
resource language pairs, with a reduction in training time of 60%. Our
improvements hold across multiple data sizes and two language pairs.
