Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: A Principle Solution for Enroll-Test Mismatch in Speaker Recognition   Mismatch between enrollment and test conditions causes serious performance
degradation on speaker recognition systems. This paper presents a statistics
decomposition (SD) approach to solve this problem. This approach decomposes the
PLDA score into three components that corresponding to enrollment, prediction
and normalization respectively. Given that correct statistics are used in each
component, the resultant score is theoretically optimal. A comprehensive
experimental study was conducted on three datasets with different types of
mismatch: (1) physical channel mismatch, (2) speaking behavior mismatch, (3)
near-far recording mismatch. The results demonstrated that the proposed SD
approach is highly effective, and outperforms the ad-hoc multi-condition
training approach that is commonly adopted but not optimal in theory.

24
The categories of this paper are: dg-ga
31
The categories of this paper are: ao-sci
34
The categories of this paper are: chem-ph
5
The categories of this paper are: gr-qc
27
The categories of this paper are: comp-gas
30
The categories of this paper are: q-alg
29
The categories of this paper are: funct-an
13
The categories of this paper are: quant-ph
15
The categories of this paper are: nucl-ex
4
The categories of this paper are: cond-mat
26
The categories of this paper are: bayes-an
35
The categories of this paper are: mtrl-th
32
The categories of this paper are: atom-ph
9
The categories of this paper are: nlin
2
The categories of this paper are: math
10
The categories of this paper are: q-bio