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Accession Number PB2013-101834
Title Significance Test in Speaker Recognition Data Analysis with Data Dependency.
Publication Date Oct 2012
Media Count 17p
Personal Author A. F. Martin C. S. Greenberg J. C. Wu R. N. Kacker V. M. Stanford
Abstract To evaluate the performance of speaker recognition systems, a detection cost function defined as a weighted sum of the probabilities of type I and type II errors is employed. The speaker datasets may have data dependency due to multiple uses of the same subjects. Using the standard errors of the detection cost function computed by means of the two-layer nonparametric two-sample bootstrap method, a significance test is performed to determine whether the difference between the measured performance levels of two speaker recognition algorithms is statistically significant. While conducting the significance test, the correlation coefficient between two detection cost functions for two algorithms, respectively, is taken into account. Examples are provided.
Keywords Algorithms
Bootstrap method
Data analysis
Data dependency
Significance test
Speaker recognition

 
Source Agency National Institute of Standards and Technology
NTIS Subject Category 72F - Statistical Analysis
Corporate Author National Inst. of Standards and Technology (ITL), Gaithersburg, MD. Information Access Div.
Document Type Technical report
Title Note N/A
NTIS Issue Number 1303
Contract Number N/A

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