  In our previous work, we proposed a discriminative autoencoder (DcAE) for
speech recognition. DcAE combines two training schemes into one. First, since
DcAE aims to learn encoder-decoder mappings, the squared error between the
reconstructed speech and the input speech is minimized. Second, in the code
layer, frame-based phonetic embeddings are obtained by minimizing the
categorical cross-entropy between ground truth labels and predicted
triphone-state scores. DcAE is developed based on the Kaldi toolkit by treating
various TDNN models as encoders. In this paper, we further propose three new
versions of DcAE. First, a new objective function that considers both
categorical cross-entropy and mutual information between ground truth and
predicted triphone-state sequences is used. The resulting DcAE is called a
chain-based DcAE (c-DcAE). For application to robust speech recognition, we
further extend c-DcAE to hierarchical and parallel structures, resulting in
hc-DcAE and pc-DcAE. In these two models, both the error between the
reconstructed noisy speech and the input noisy speech and the error between the
enhanced speech and the reference clean speech are taken into the objective
function. Experimental results on the WSJ and Aurora-4 corpora show that our
DcAE models outperform baseline systems.
