  In this work, we first show that on the widely used LibriSpeech benchmark,
our transformer-based context-dependent connectionist temporal classification
(CTC) system produces state-of-the-art results. We then show that using
wordpieces as modeling units combined with CTC training, we can greatly
simplify the engineering pipeline compared to conventional frame-based
cross-entropy training by excluding all the GMM bootstrapping, decision tree
building and force alignment steps, while still achieving very competitive
word-error-rate. Additionally, using wordpieces as modeling units can
significantly improve runtime efficiency since we can use larger stride without
losing accuracy. We further confirm these findings on two internal VideoASR
datasets: German, which is similar to English as a fusional language, and
Turkish, which is an agglutinative language.
