  Beam search, which is the dominant ASR decoding algorithm for end-to-end
models, generates tree-structured hypotheses. However, recent studies have
shown that decoding with hypothesis merging can achieve a more efficient search
with comparable or better performance. But, the full context in recurrent
networks is not compatible with hypothesis merging. We propose to use
vector-quantized long short-term memory units (VQ-LSTM) in the prediction
network of RNN transducers. By training the discrete representation jointly
with the ASR network, hypotheses can be actively merged for lattice generation.
Our experiments on the Switchboard corpus show that the proposed VQ RNN
transducers improve ASR performance over transducers with regular prediction
networks while also producing denser lattices with a very low oracle word error
rate (WER) for the same beam size. Additional language model rescoring
experiments also demonstrate the effectiveness of the proposed lattice
generation scheme.
