  In this paper we propose the Structured Deep Neural Network (Structured DNN)
as a structured and deep learning algorithm, learning 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 structure 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 learned 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 algorithm. It was shown to beat
structured SVM in preliminary experiments on TIMIT.
