  Attention-based sequence-to-sequence models have shown promising results in
automatic speech recognition. Using these architectures, one-dimensional input
and output sequences are related by an attention approach, thereby replacing
more explicit alignment processes, like in classical HMM-based modeling. In
contrast, here we apply a novel two-dimensional long short-term memory (2DLSTM)
architecture to directly model the input/output relation between audio/feature
vector sequences and word sequences. The proposed model is an alternative model
such that instead of using any type of attention components, we apply a 2DLSTM
layer to assimilate the context from both input observations and output
transcriptions. The experimental evaluation on the Switchboard 300h automatic
speech recognition task shows word error rates for the 2DLSTM model that are
competitive to end-to-end attention-based model.
