Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: A Comparison between Deep Neural Nets and Kernel Acoustic Models for
  Speech Recognition   We study large-scale kernel methods for acoustic modeling and compare to DNNs
on performance metrics related to both acoustic modeling and recognition.
Measuring perplexity and frame-level classification accuracy, kernel-based
acoustic models are as effective as their DNN counterparts. However, on
token-error-rates DNN models can be significantly better. We have discovered
that this might be attributed to DNN's unique strength in reducing both the
perplexity and the entropy of the predicted posterior probabilities. Motivated
by our findings, we propose a new technique, entropy regularized perplexity,
for model selection. This technique can noticeably improve the recognition
performance of both types of models, and reduces the gap between them. While
effective on Broadcast News, this technique could be also applicable to other
tasks.

2
The categories of this paper are: math
15
The categories of this paper are: nucl-ex
36
The categories of this paper are: cmp-lg
6
The categories of this paper are: astro-ph
26
The categories of this paper are: bayes-an
29
The categories of this paper are: funct-an
18
The categories of this paper are: econ
19
The categories of this paper are: eess
22
The categories of this paper are: chao-dyn
34
The categories of this paper are: chem-ph
21
The categories of this paper are: adap-org
20
The categories of this paper are: acc-phys
4
The categories of this paper are: cond-mat
0
The categories of this paper are: hep-ph
31
The categories of this paper are: ao-sci
33
The categories of this paper are: plasm-ph