  Attention-based methods and Connectionist Temporal Classification (CTC)
network have been promising research directions for end-to-end Automatic Speech
Recognition (ASR). The joint CTC/Attention model has achieved great success by
utilizing both architectures during multi-task training and joint decoding. In
this work, we present a novel Multi-Encoder Multi-Resolution (MEMR) framework
based on the joint CTC/Attention model. Two heterogeneous encoders with
different architectures, temporal resolutions and separate CTC networks work in
parallel to extract complimentary acoustic information. A hierarchical
attention mechanism is then used to combine the encoder-level information. To
demonstrate the effectiveness of the proposed model, experiments are conducted
on Wall Street Journal (WSJ) and CHiME-4, resulting in relative Word Error Rate
(WER) reduction of 18.0-32.1%. Moreover, the proposed MEMR model achieves 3.6%
WER in the WSJ eval92 test set, which is the best WER reported for an
end-to-end system on this benchmark.
