Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Sequence Training and Adaptation of Highway Deep Neural Networks   Highway deep neural network (HDNN) is a type of depth-gated feedforward
neural network, which has shown to be easier to train with more hidden layers
and also generalise better compared to conventional plain deep neural networks
(DNNs). Previously, we investigated a structured HDNN architecture for speech
recognition, in which the two gate functions were tied across all the hidden
layers, and we were able to train a much smaller model without sacrificing the
recognition accuracy. In this paper, we carry on the study of this architecture
with sequence-discriminative training criterion and speaker adaptation
techniques on the AMI meeting speech recognition corpus. We show that these two
techniques improve speech recognition accuracy on top of the model trained with
the cross entropy criterion. Furthermore, we demonstrate that the two gate
functions that are tied across all the hidden layers are able to control the
information flow over the whole network, and we can achieve considerable
improvements by only updating these gate functions in both sequence training
and adaptation experiments.

16
The categories of this paper are: stat
31
The categories of this paper are: ao-sci
10
The categories of this paper are: q-bio
29
The categories of this paper are: funct-an
22
The categories of this paper are: chao-dyn
32
The categories of this paper are: atom-ph
18
The categories of this paper are: econ
25
The categories of this paper are: patt-sol
12
The categories of this paper are: nucl-th
20
The categories of this paper are: acc-phys
13
The categories of this paper are: quant-ph
35
The categories of this paper are: mtrl-th
3
The categories of this paper are: physics
14
The categories of this paper are: math-ph
34
The categories of this paper are: chem-ph
8
The categories of this paper are: hep-ex