多子系统似然度评分融合说话人识别

针对短电话语音条件下文本无关说话人识别问题中语音数据不充分和电话信道失配问题，提出了一种基于话者聚类的多子系统输出似然度评分融合策略。采用KLD和GLR测度下的模型相似度聚类方法对目标话者聚类，并在每个话者类内构建由MFCC、LPCC和SSFE三个不同类型特征参数子系统组成的输出似然度评分融合系统，通过不同参数子系统的互补，即MFCC和LPCC参数的识别准确性结合SSFE的良好鲁棒性，以及不同话者类采用不同的输出似然度评分融合网络，提高了系统的整体性能。使用NIST SRE 05数据作为评估数据,实验结果表明，与传统的不分类多系统输出似然度评分融合相比，采用KLD和GLR测度的话者聚类融合策略使系统等误识率分别下降了10.3%和8.7%。

Speaker recognition based on multi-subsystems likelihood scores fusion

To describe an approach of speaker clustering based on multi-subsystems likelihood scores fusion to the text-independent speaker recognition system with short speech data in various telephone microphone channels. The registered speakers were aggregated into clusters with 2 types of speaker model similarity measures, namely, Kullback-Leibler Divergence (KLD) and Generalized Likelihood Ratio (GLR). A single-layer perception network was built for each cluster, fusing the likelihood scores of 3 sub-systems with the speaker features of MFCC, LPCC and SSFE, respectively. Concerning the robustness of SSFE system and recognition accuracy of the other 2 systems, the 3 sub-systems complement each other with the fusion network in each cluster. Experimental results on NIST SRE 05's database show a relative equal error rate reduction of 10.3% and 8.7%, with KLD and GLR, respectively, with respect to an all-speaker-shared fusion network.