Toward Open-Set Text-Independent Speaker Identification in Tactical Communications

Wolf, Matt B.; Park, WonKyung; Oh,; Blowers, Misty

doi:10.1109/cisda.2007.368129

Cited by 2 publications

(1 citation statement)

References 20 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Open-set classification is essential in such applications as speaker identification (Cepeda, 2007;Deng and Hu, 2003;Wolf et al, 2007), facial recognition (Zhao and Principe, 2001), and authorship attribution (Koppel et al, 2009;Stamatatos, 2009). …”

Section: Introductionmentioning

confidence: 99%

Open-Set Nearest Shrunken Centroid Classification

Schaalje

Fields

2012

Communications in Statistics - Theory and Methods

View full text Add to dashboard Cite

Nearest Shrunken Centroid (NSC) classification has proven successful in ultrahigh-dimensional classification problems involving thousands of features measured on relatively few individuals, such as in the analysis of DNA microarrays. The method requires the set of candidate classes to be closed. However, open-set classification is essential in many other applications including speaker identification, facial recognition, and authorship attribution. The authors review closed-set NSC classification, and then propose a diagnostic for whether open-set classification is needed. The diagnostic involves graphical and statistical comparison of posterior predictions of the test vectors to the observed test vectors. The authors propose a simple modification to NSC that allows the set of classes to be open. The openset modification posits an unobserved class with a distribution of features justbarely consistent with the test sample. A tuning constant reflects the combined considerations of power, specificity, multiplicity, number of features, and sample size. The authors illustrate and investigate properties of the diagnostic test and openset NSC classification procedure using several example data sets. The diagnostic and the open-set NSC procedures are shown to be useful for identifying vectors that are not consistent with any of the candidate classes.

show abstract