Analysis of multitarget detection for speaker and language recognition

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 detector’s 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.