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Significance Test in Speaker Recognition Data Analysis with Data Dependency

Published

Author(s)

Jin Chu Wu, Alvin F. Martin, Craig S. Greenberg, Raghu N. Kacker, Vincent 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.
Citation
NIST Interagency/Internal Report (NISTIR) - 7884
Report Number
7884

Keywords

Significance test, Speaker recognition, Data dependency, Standard error, Bootstrap.

Citation

, J. , Martin, A. , Greenberg, C. , Kacker, R. and Stanford, V. (2012), Significance Test in Speaker Recognition Data Analysis with Data Dependency, NIST Interagency/Internal Report (NISTIR), National Institute of Standards and Technology, Gaithersburg, MD, [online], https://doi.org/10.6028/NIST.IR.7884 (Accessed February 22, 2024)
Created October 17, 2012, Updated November 10, 2018