  This work concentrates on reducing the RTF and word error rate of a hybrid
HMM-DNN. Our baseline system uses an architecture with TDNN and LSTM layers. We
find this architecture particularly useful for lightly reverberated
environments. However, these models tend to demand more computation than is
desirable. In this work, we explore alternate architectures employing singular
value decomposition (SVD) is applied to the TDNN layers to reduce the RTF, as
well as to the affine transforms of every LSTM cell. We compare this approach
with specifying bottleneck layers similar to those introduced by SVD before
training. Additionally, we reduced the search space of the decoding graph to
make it a better fit to operate in real-time applications. We report -61.57%
relative reduction in RTF and almost 1% relative decrease in WER for our
architecture trained on Fisher data along with reverberated versions of this
dataset in order to match one of our target test distributions.
