We present our transducer model on Librispeech. We study variants to include an external language model (LM) with shallow fusion and subtract an estimated internal LM. This is justified by a Bayesian interpretation where the transducer model prior is given by the estimated internal LM. The subtraction of the internal LM gives us over 14% relative improvement over normal shallow fusion. Our transducer has a separate probability distribution for the non-blank labels which allows for easier combination with the external LM, and easier estimation of the internal LM. We additionally take care of including the end-of-sentence (EOS) probability of the external LM in the last blank probability which further improves the performance. All our code and setups are published.

翻译：我们在Librispeech上展示了我们的传感器模型。 我们研究变量, 以包括外语模型(LM), 有浅聚变, 并减去内部估计的LM。 一种巴伊西亚解释证明这一点是合理的, 估计内部LM给出了先前的传感器模型。 内部LM的减值让我们比正常的浅聚变提高了14%以上。 我们的传感器对非空白标签的概率分布是分开的, 它使得与外部LM更容易组合, 并且更容易估计内部LM。 我们另外还注意将外部LM的末端概率(EOS)纳入最后的空白概率, 从而进一步提高了性能。 我们的所有代码和设置都公布了 。