  An on-device DNN-HMM speech recognition system efficiently works with a
limited vocabulary in the presence of a variety of predictable noise. In such a
case, vocabulary and environment adaptation is highly effective. In this paper,
we propose a novel method of end-to-end (E2E) adaptation, which adjusts not
only an acoustic model (AM) but also a weighted finite-state transducer (WFST).
We convert a pretrained WFST to a trainable neural network and adapt the system
to target environments/vocabulary by E2E joint training with an AM. We
replicate Viterbi decoding with forward--backward neural network computation,
which is similar to recurrent neural networks (RNNs). By pooling output score
sequences, a vocabulary posterior for each utterance is obtained and used for
discriminative loss computation. Experiments using 2--10 hours of
English/Japanese adaptation datasets indicate that the fine-tuning of only
WFSTs and that of only AMs are both comparable to a state-of-the-art adaptation
method, and E2E joint training of the two components achieves the best
recognition performance. We also adapt each language system to the other
language using the adaptation data, and the results show that the proposed
method also works well for language adaptations.
