  Performance of learning based Automatic Speech Recognition (ASR) is
susceptible to noise, especially when it is introduced in the testing data
while not presented in the training data. This work focuses on a feature
enhancement for noise robust end-to-end ASR system by introducing a novel
variant of denoising autoencoder (DAE). The proposed method uses skip
connections in both encoder and decoder sides by passing speech information of
the target frame from input to the model. It also uses a new objective function
in training model that uses a correlation distance measure in penalty terms by
measuring dependency of the latent target features and the model (latent
features and enhanced features obtained from the DAE). Performance of the
proposed method was compared against a conventional model and a state of the
art model under both seen and unseen noisy environments of 7 different types of
background noise with different SNR levels (0, 5, 10 and 20 dB). The proposed
method also is tested using linear and non-linear penalty terms as well, where,
they both show an improvement on the overall average WER under noisy conditions
both seen and unseen in comparison to the state-of-the-art model.
