  When recurrent neural network transducers (RNNTs) are trained using the
typical maximum likelihood criterion, the prediction network is trained only on
ground truth label sequences. This leads to a mismatch during inference, known
as exposure bias, when the model must deal with label sequences containing
errors. In this paper we investigate approaches to reducing exposure bias in
training to improve the generalization of RNNT models for automatic speech
recognition (ASR). A label-preserving input perturbation to the prediction
network is introduced. The input token sequences are perturbed using SwitchOut
and scheduled sampling based on an additional token language model. Experiments
conducted on the 300-hour Switchboard dataset demonstrate their effectiveness.
By reducing the exposure bias, we show that we can further improve the accuracy
of a high-performance RNNT ASR model and obtain state-of-the-art results on the
300-hour Switchboard dataset.
