Significance Test with Data Dependency in Speaker Recognition Evaluation

Published

July 25, 2013

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 systems’ detection cost functions is taken into account. Examples are provided.

Proceedings Title

Proc. of SPIE Vol. 8734

Conference Dates

May 1-2, 2013

Conference Location

Baltimore, MD

Conference Title

SPIE Defense Security Sensing, 2013

Pub Type

Conferences

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Keywords

Significance test, Data dependency, Speaker recognition evaluation, Measurement uncertainty, Standard error, ROC analysis, Bootstrap, Biometrics.

Citation

, J. , Martin, A. , Greenberg, C. , Kacker, R. and Stanford, V. (2013), Significance Test with Data Dependency in Speaker Recognition Evaluation, Proc. of SPIE Vol. 8734, Baltimore, MD, [online], https://tsapps.nist.gov/publication/get_pdf.cfm?pub_id=914184 (Accessed March 13, 2025)