Proceedings Paper
Significance test with data dependency in speaker recognition evaluation| Format | Member Price | Non-Member Price |
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Paper 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.
Paper Details
Date Published: 23 May 2013
PDF: 13 pages
Proc. SPIE 8734, Active and Passive Signatures IV, 87340I (23 May 2013); doi: 10.1117/12.2014536
Published in SPIE Proceedings Vol. 8734:
Active and Passive Signatures IV
G. Charmaine Gilbreath; Chadwick Todd Hawley, Editor(s)
PDF: 13 pages
Proc. SPIE 8734, Active and Passive Signatures IV, 87340I (23 May 2013); doi: 10.1117/12.2014536
Show Author Affiliations
Jin Chu Wu, National Institute of Standards and Technology (United States)
Alvin F. Martin, National Institute of Standards and Technology (United States)
Craig S. Greenberg, National Institute of Standards and Technology (United States)
Alvin F. Martin, National Institute of Standards and Technology (United States)
Craig S. Greenberg, National Institute of Standards and Technology (United States)
Raghu N. Kacker, National Institute of Standards and Technology (United States)
Vincent M. Stanford, National Institute of Standards and Technology (United States)
Vincent M. Stanford, National Institute of Standards and Technology (United States)
Published in SPIE Proceedings Vol. 8734:
Active and Passive Signatures IV
G. Charmaine Gilbreath; Chadwick Todd Hawley, Editor(s)
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