The general multitarget detection (open-set identification) task is the intersection of the more familiar tasks of close-set identification and open-set verification/detection. In the multitarget detection task, an input of unknown class is processed by a bank of parallel detectors and a decision is required as to whether the input is from among the target classes and, if so, which one. In this paper, we show analytically how the performance of a multitarget detector can be predicted from the open-set detection performance of the individual detectors of which it is constructed. We use this analytical framework to establish the relationship between the multitarget detectors closed-set identification error rate and its open-set detector miss and false alarm probabilities. Experiments performed using standard speaker and language corpora are described that demonstrate the validity of the analysis.
Cite as: Singer, E., Reynolds, D.A. (2004) Analysis of multitarget detection for speaker and language recognition. Proc. The Speaker and Language Recognition Workshop (Odyssey 2004), 301-308
@inproceedings{singer04_odyssey,
author={Elliot Singer and Douglas A. Reynolds},
title={{Analysis of multitarget detection for speaker and language recognition}},
year=2004,
booktitle={Proc. The Speaker and Language Recognition Workshop (Odyssey 2004)},
pages={301--308}
}