Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: A Comparison of Adaptation Techniques and Recurrent Neural Network
  Architectures   Recently, recurrent neural networks have become state-of-the-art in acoustic
modeling for automatic speech recognition. The long short-term memory (LSTM)
units are the most popular ones. However, alternative units like gated
recurrent unit (GRU) and its modifications outperformed LSTM in some
publications. In this paper, we compared five neural network (NN) architectures
with various adaptation and feature normalization techniques. We have evaluated
feature-space maximum likelihood linear regression, five variants of i-vector
adaptation and two variants of cepstral mean normalization. The most adaptation
and normalization techniques were developed for feed-forward NNs and, according
to results in this paper, not all of them worked also with RNNs. For
experiments, we have chosen a well known and available TIMIT phone recognition
task. The phone recognition is much more sensitive to the quality of AM than
large vocabulary task with a complex language model. Also, we published the
open-source scripts to easily replicate the results and to help continue the
development.

13
The categories of this paper are: quant-ph
7
The categories of this paper are: hep-th
34
The categories of this paper are: chem-ph
31
The categories of this paper are: ao-sci
30
The categories of this paper are: q-alg
6
The categories of this paper are: astro-ph
35
The categories of this paper are: mtrl-th
25
The categories of this paper are: patt-sol
3
The categories of this paper are: physics
15
The categories of this paper are: nucl-ex
33
The categories of this paper are: plasm-ph
32
The categories of this paper are: atom-ph
22
The categories of this paper are: chao-dyn
12
The categories of this paper are: nucl-th
27
The categories of this paper are: comp-gas
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The categories of this paper are: q-fin