Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: On the Use of Different Feature Extraction Methods for Linear and Non
  Linear kernels   The speech feature extraction has been a key focus in robust speech
recognition research; it significantly affects the recognition performance. In
this paper, we first study a set of different features extraction methods such
as linear predictive coding (LPC), mel frequency cepstral coefficient (MFCC)
and perceptual linear prediction (PLP) with several features normalization
techniques like rasta filtering and cepstral mean subtraction (CMS). Based on
this, a comparative evaluation of these features is performed on the task of
text independent speaker identification using a combination between gaussian
mixture models (GMM) and linear and non-linear kernels based on support vector
machine (SVM).

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The categories of this paper are: cmp-lg
13
The categories of this paper are: quant-ph
17
The categories of this paper are: q-fin
4
The categories of this paper are: cond-mat
11
The categories of this paper are: hep-lat
34
The categories of this paper are: chem-ph
2
The categories of this paper are: math
6
The categories of this paper are: astro-ph
9
The categories of this paper are: nlin
8
The categories of this paper are: hep-ex
0
The categories of this paper are: hep-ph
28
The categories of this paper are: alg-geom
29
The categories of this paper are: funct-an
20
The categories of this paper are: acc-phys
26
The categories of this paper are: bayes-an
15
The categories of this paper are: nucl-ex