Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Bidirectional Quaternion Long-Short Term Memory Recurrent Neural
  Networks for Speech Recognition   Recurrent neural networks (RNN) are at the core of modern automatic speech
recognition (ASR) systems. In particular, long-short term memory (LSTM)
recurrent neural networks have achieved state-of-the-art results in many speech
recognition tasks, due to their efficient representation of long and short term
dependencies in sequences of inter-dependent features. Nonetheless, internal
dependencies within the element composing multidimensional features are weakly
considered by traditional real-valued representations. We propose a novel
quaternion long-short term memory (QLSTM) recurrent neural network that takes
into account both the external relations between the features composing a
sequence, and these internal latent structural dependencies with the quaternion
algebra. QLSTMs are compared to LSTMs during a memory copy-task and a realistic
application of speech recognition on the Wall Street Journal (WSJ) dataset.
QLSTM reaches better performances during the two experiments with up to $2.8$
times less learning parameters, leading to a more expressive representation of
the information.

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The categories of this paper are: hep-th
36
The categories of this paper are: cmp-lg
2
The categories of this paper are: math
10
The categories of this paper are: q-bio
6
The categories of this paper are: astro-ph
33
The categories of this paper are: plasm-ph
4
The categories of this paper are: cond-mat
12
The categories of this paper are: nucl-th
26
The categories of this paper are: bayes-an
15
The categories of this paper are: nucl-ex
31
The categories of this paper are: ao-sci
28
The categories of this paper are: alg-geom
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The categories of this paper are: math-ph
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The categories of this paper are: acc-phys
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The categories of this paper are: patt-sol
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The categories of this paper are: hep-ex