  This paper presents TEVR, a speech recognition model designed to minimize the
variation in token entropy w.r.t. to the language model. This takes advantage
of the fact that if the language model will reliably and accurately predict a
token anyway, then the acoustic model doesn't need to be accurate in
recognizing it. We train German ASR models with 900 million parameters and show
that on CommonVoice German, TEVR scores a very competitive 3.64% word error
rate, which outperforms the best reported results by a relative 16.89%
reduction in word error rate. We hope that releasing our fully trained speech
recognition pipeline to the community will lead to privacy-preserving offline
virtual assistants in the future.
