Access the full text.
Sign up today, get DeepDyve free for 14 days.
References for this paper are not available at this time. We will be adding them shortly, thank you for your patience.
The consequences of ignoring correlations between features in traditional forensic speaker recognition are investigated. Two likelihood ratio-based discrimination experiments on the same multivariate formant data are described, one taking correlation into account and the other not doing so. The discrimination is performed using Naive Bayes univariate, and multivariate generative Likelihood Ratios (LRs) as discriminant functions, exemplified with Tippett plots and evaluated with the Cllr cost function. It is shown that ignoring within-segment correlation can result in considerable over- or under-estimation of the strength of evidence when traditional features are used, and there is poorer overall discrimination between same-speaker and different-speaker pairs. The use of logistic-regression fusion to handle between-segment correlation is also demonstrated.
International Journal of Biometrics – Inderscience Publishers
Published: Jan 1, 2010
Read and print from thousands of top scholarly journals.
Already have an account? Log in
Bookmark this article. You can see your Bookmarks on your DeepDyve Library.
To save an article, log in first, or sign up for a DeepDyve account if you don’t already have one.
Copy and paste the desired citation format or use the link below to download a file formatted for EndNote
Access the full text.
Sign up today, get DeepDyve free for 14 days.
All DeepDyve websites use cookies to improve your online experience. They were placed on your computer when you launched this website. You can change your cookie settings through your browser.