  In recent years, deep learning based machine lipreading has gained
prominence. To this end, several architectures such as LipNet, LCANet and
others have been proposed which perform extremely well compared to traditional
lipreading DNN-HMM hybrid systems trained on DCT features. In this work, we
propose a simpler architecture of 3D-2D-CNN-BLSTM network with a bottleneck
layer. We also present analysis of two different approaches for lipreading on
this architecture. In the first approach, 3D-2D-CNN-BLSTM network is trained
with CTC loss on characters (ch-CTC). Then BLSTM-HMM model is trained on
bottleneck lip features (extracted from 3D-2D-CNN-BLSTM ch-CTC network) in a
traditional ASR training pipeline. In the second approach, same 3D-2D-CNN-BLSTM
network is trained with CTC loss on word labels (w-CTC). The first approach
shows that bottleneck features perform better compared to DCT features. Using
the second approach on Grid corpus' seen speaker test set, we report $1.3\%$
WER - a $55\%$ improvement relative to LCANet. On unseen speaker test set we
report $8.6\%$ WER which is $24.5\%$ improvement relative to LipNet. We also
verify the method on a second dataset of $81$ speakers which we collected.
Finally, we also discuss the effect of feature duplication on BLSTM-HMM model
performance.
