  This paper presents our latest investigation on Densely Connected
Convolutional Networks (DenseNets) for acoustic modelling (AM) in automatic
speech recognition. DenseN-ets are very deep, compact convolutional neural
networks, which have demonstrated incredible improvements over the
state-of-the-art results on several data sets in computer vision. Our
experimental results show that DenseNet can be used for AM significantly
outperforming other neural-based models such as DNNs, CNNs, VGGs. Furthermore,
results on Wall Street Journal revealed that with only a half of the training
data DenseNet was able to outperform other models trained with the full data
set by a large margin.
