Speaker verification based on linguistic attribute projection
声纹识别中的语言属性映射

Linguistic attribute mismatch reduces the performance of speaker recognition system in short-time voiceprint recognition, so linguistic attribute mismatch has become the hot spot in research. A linguistic attribute projection approach was proposed to eliminate the linguistic attribute information in acoustic feature parameters. We built train samples with mean supervector, and used gauss mixture model for unequal length feature of every word. Moreover, the introduction of weighted matrix gets linguistic attribute space, and eliminates the effects of linguistic attribute in statistical parameters supervector space through projection method. Experimental results demonstrate that the equal error rate of the proposed system can be reduced by 23.07% and 20.39% against the baseline system in male and female trials part respectively.