【摘要】 针对由语种类内多样性引起的测试样本和训练模型不匹配的问题,提出一种基于局部距离离群因子准则(LDOF,local distance-based outlier factor)的自适应高斯后端语种识别方法。定义LDOF准则,实现有效的参数寻优过程并动态地在多类语种训练集上挑选出与测试样本特性相近的训练样本,调整原高斯后端,进而得到改进的语种识别方法。在NIST LRE 2009的6个易混淆语种任务集上的实验结果表明,所提方法的等错误概率(EER,equal error rate)和平均检测代价有显著提升。更多还原

【Abstract】 In order to alleviate the mismatch in model between training and testing samples caused by inter-language variations, adaptive Gaussian back-end based on LDOF criterion was proposed for language recognition. The local distance-based outlier factor(LDOF) criterion was defined to find the appropriate model parameters and dynamically select the training data subset similar to the testing samples from multiple class training sets. Then original back-end was adjusted to obtain a more matched recognition model. Experimental results on NIST LRE 2009 easily-confused language data set show that proposed method achieves an obvious performance improvement on both the equal error rate(ERR) and average decision cost function.更多还原