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.

该论文提出了一种名为 TEVR 的语音识别模型，旨在减少与语言模型相关的令牌熵的差异。我们使用了 9 亿个参数训练了德语 ASR 模型，并展示了在 CommonVoice 德语数据集上，TEVR 获得了非常有竞争力的 3.64% 词错误率，相对于词错误率的最佳报告结果减少了 16.89％。我们希望将我们完全训练好的语音识别管道释放给社区，以期未来实现隐私保护的离线虚拟助手。