  This paper explores the use of multi-view features and their discriminative
transforms in a convolutional deep neural network (CNN) architecture for a
continuous large vocabulary speech recognition task. Mel-filterbank energies
and perceptually motivated forced damped oscillator coefficient (DOC) features
are used after feature-space maximum-likelihood linear regression (fMLLR)
transforms, which are combined and fed as a multi-view feature to a single CNN
acoustic model. Use of multi-view feature representation demonstrated
significant reduction in word error rates (WERs) compared to the use of
individual features by themselves. In addition, when articulatory information
was used as an additional input to a fused deep neural network (DNN) and CNN
acoustic model, it was found to demonstrate further reduction in WER for the
Switchboard subset and the CallHome subset (containing partly non-native
accented speech) of the NIST 2000 conversational telephone speech test set,
reducing the error rate by 12% relative to the baseline in both cases. This
work shows that multi-view features in association with articulatory
information can improve speech recognition robustness to spontaneous and
non-native speech.
