  In this paper, we explore several new schemes to train a seq2seq model to
integrate a pre-trained LM. Our proposed fusion methods focus on the memory
cell state and the hidden state in the seq2seq decoder long short-term memory
(LSTM), and the memory cell state is updated by the LM unlike the prior
studies. This means the memory retained by the main seq2seq would be adjusted
by the external LM. These fusion methods have several variants depending on the
architecture of this memory cell update and the use of memory cell and hidden
states which directly affects the final label inference. We performed the
experiments to show the effectiveness of the proposed methods in a mono-lingual
ASR setup on the Librispeech corpus and in a transfer learning setup from a
multilingual ASR (MLASR) base model to a low-resourced language. In
Librispeech, our best model improved WER by 3.7%, 2.4% for test clean, test
other relatively to the shallow fusion baseline, with multi-level decoding. In
transfer learning from an MLASR base model to the IARPA Babel Swahili model,
the best scheme improved the transferred model on eval set by 9.9%, 9.8% in
CER, WER relatively to the 2-stage transfer baseline.
