Background population: how does it affect LR based forensic voice comparison?. Kinoshita, Y. and Ishihara, S. International Journal of Speech Language and the Law, 21(2):191-224.
doi  abstract   bibtex   
This article investigates to what extent and in what ways the size of the background population affects the outcome of likelihood ratio (LR) based forensic voice comparison. While sample size is known to affect the accuracy of statistical modelling, specific effects in the context of forensic voice comparison are not yet understood. Forensic voice comparison analysts need to work with limited data, but it is unclear how this might impact on the LR-based evaluation of evidence. In this article, we report LR-based speaker comparison experiments using variously sized datasets for background population. They use features derived from long term F0 distribution. We examined their performance in terms of accuracy (closeness to the true value) and precision (reproducibility).
@article{kinoshita_background_2015,
	Author = {Kinoshita, Yuko and Ishihara, Shunichi},
	Date = {2015},
	Date-Modified = {2017-04-19 08:04:07 +0000},
	Doi = {10.1558/ijsll.v21i2.191},
	Issn = {17488885},
	Journal = {International Journal of Speech Language and the Law},
	Keywords = {forensic, forensic phonetics},
	Language = {en},
	Number = {2},
	Pages = {191-224},
	Title = {Background population: how does it affect LR based forensic voice comparison?},
	Volume = {21},
	Abstract = {This article investigates to what extent and in what ways the size of the background population affects the outcome of likelihood ratio (LR) based forensic voice comparison. While sample size is known to affect the accuracy of statistical modelling, specific effects in the context of forensic voice comparison are not yet understood. Forensic voice comparison analysts need to work with limited data, but it is unclear how this might impact on the LR-based evaluation of evidence. In this article, we report LR-based speaker comparison experiments using variously sized datasets for background population. They use features derived from long term F0 distribution. We examined their performance in terms of accuracy (closeness to the true value) and precision (reproducibility).},
	Bdsk-Url-1 = {http://www.equinoxpub.com/journals/index.php/IJSLL/article/view/19177},
	Bdsk-Url-2 = {http://dx.doi.org/10.1558/ijsll.v21i2.191}}
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