  In this paper, we tackle the problem of handling narrowband and wideband
speech by building a single acoustic model (AM), also called mixed bandwidth
AM. In the proposed approach, an auxiliary input feature is used to provide the
bandwidth information to the model, and bandwidth embeddings are jointly
learned as part of acoustic model training. Experimental evaluations show that
using bandwidth embeddings helps the model to handle the variability of the
narrow and wideband speech, and makes it possible to train a mixed-bandwidth
AM. Furthermore, we propose to use parallel convolutional layers to handle the
mismatch between the narrow and wideband speech better, where separate
convolution layers are used for each type of input speech signal. Our best
system achieves 13% relative improvement on narrowband speech, while not
degrading on wideband speech.
