  Hybrid Autoregressive Transducer (HAT) is a recently proposed end-to-end
acoustic model that extends the standard Recurrent Neural Network Transducer
(RNN-T) for the purpose of the external language model (LM) fusion. In HAT, the
blank probability and the label probability are estimated using two separate
probability distributions, which provides a more accurate solution for internal
LM score estimation, and thus works better when combining with an external LM.
Previous work mainly focuses on HAT model training with the negative
log-likelihood loss, while in this paper, we study the minimum word error rate
(MWER) training of HAT -- a criterion that is closer to the evaluation metric
for speech recognition, and has been successfully applied to other types of
end-to-end models such as sequence-to-sequence (S2S) and RNN-T models. From
experiments with around 30,000 hours of training data, we show that MWER
training can improve the accuracy of HAT models, while at the same time,
improving the robustness of the model against the decoding hyper-parameters
such as length normalization and decoding beam during inference.
