Training candidate selection for effective rejection in open-set language identification

Research in open-set language identification (LID) generally focuses more on accurate in-set modeling versus improved out-of-set (OOS) rejection. Unknown or OOS language rejection is a challenge, since research developers seldom commit equivalent OOS corpus development effort versus the desired in-set languages. To address this, we propose an OOS candidate selection method for universal OOS language coverage. Since effective selection always requires abundant knowledge of inter-language relationships, three broad measurements across world languages are considered. Finally, the advanced OOS selection method is evaluated on a database derived from a large-scale corpus (LRE-09) with a state-of-the-art i-Vector system followed by two back-ends. The baseline system is realized using a random selection of OOS candidates. With the proposed selection method and probabilistic linear discriminative analysis (PLDA) back-end, the OOS rejection performance is improved with false alarm and miss rates achieving a relative reduction of 32.6% and 4.4%, respectively. In addition, the overall classification performance are relatively improved 8.4% and 7.5% according to the two back-ends based on an average cost function.