Score fusion and calibration in multiple language detectors with large performance variation

In a large-scale language detection task, performance variation found between different component systems and different target languages has an adverse effect to the pooled error statistics. Special care has to be taken in score fusion and calibration. In this paper, we use a prosodic LID system to fuse with a phonotactic LID system using NIST Language Recognition Evaluation 2009 experimental data. Among four logistic regression models, the one which gives the lowest Cavg is chosen. We further explore our previously proposed calibration algorithm based on the minimum erroneous deviation criterion. The algorithm is made more robust by removing the predetermined list of target languages to be calibrated, as well as by adding an optimization constraint which enforces calibration in the data portion with a large performance variation. The fusion and calibration operations together bring a 33.9% relative Cavg reduction compared with the original result from a phonotactic LID system.