Language score calibration using adapted Gaussian back-end

Generative Gaussian back-end and discriminative logistic regression are the most used approaches for language score fusion and calibration. Combination of these two approaches can significantly improve the performance. This paper proposes the use of an adapted Gaussian back-end, where the mean of the language-dependent Gaussian is adapted from the mean of a language-specific background Gaussian viamaximum a posteriori estimation algorithm. Experiments are conducted using the LRE-07 evaluation data. Compared to the conventional Gaussian back-end approach for a closed set task, relative improvements in the Cavg of 50%, 17% and 4.2% are obtained on the 30s, 10s and 3s conditions, respectively. Besides this, the estimated scores are better calibrated. A combination with logistic regression results in a system with the best calibrated scores.