The effect of language factors for robust speaker recognition

From the results of the NIST speaker recognition evaluation in resent years, speaker recognition systems which are mainly developed based on English training data suffer the language gap problem, namely, the performance of non-English trails is much worse than that of English trails. This problem is addressed in this paper. Based on the conventional joint factor analysis model, we enrolled in the language factors which are mean to capture the language character of each testing and training speech utterance, and compensation was carried out by removing the language factors in order to shrink the difference between languages. Experiments on 2006 NIST SRE data show that, the language factor compensation alone can reduce the gap between the performance of English and non-English trails, and the score level combination with eigenchannels can further improve the performance of non-English trails, e.g., for female part, we observed about 19% relatively reduction in EER, when compared with eigenchannels session variability compensation alone.