Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Towards Structured Deep Neural Network for Automatic Speech Recognition   In this paper we propose the Structured Deep Neural Network (structured DNN)
as a structured and deep learning framework. This approach can learn to find
the best structured object (such as a label sequence) given a structured input
(such as a vector sequence) by globally considering the mapping relationships
between the structures rather than item by item.
  When automatic speech recognition is viewed as a special case of such a
structured learning problem, where we have the acoustic vector sequence as the
input and the phoneme label sequence as the output, it becomes possible to
comprehensively learn utterance by utterance as a whole, rather than frame by
frame.
  Structured Support Vector Machine (structured SVM) was proposed to perform
ASR with structured learning previously, but limited by the linear nature of
SVM. Here we propose structured DNN to use nonlinear transformations in
multi-layers as a structured and deep learning approach. This approach was
shown to beat structured SVM in preliminary experiments on TIMIT.

26
The categories of this paper are: bayes-an
17
The categories of this paper are: q-fin
22
The categories of this paper are: chao-dyn
2
The categories of this paper are: math
11
The categories of this paper are: hep-lat
29
The categories of this paper are: funct-an
34
The categories of this paper are: chem-ph
23
The categories of this paper are: solv-int
3
The categories of this paper are: physics
5
The categories of this paper are: gr-qc
21
The categories of this paper are: adap-org
30
The categories of this paper are: q-alg
9
The categories of this paper are: nlin
27
The categories of this paper are: comp-gas
37
The categories of this paper are: supr-con
32
The categories of this paper are: atom-ph