利用i-vector/PLDA模型进行说话人确认时,对于不定时间的语音,由于将长度归一化后的i-vector转化到PLDA模型时,伴随着不确定性的扭曲和缩放,影响识别率.本文通过对全变量空间矩阵T的列向量执行归一化,代替在PLDA模型上对i-vector进行长度归一化,避免因在i-vector上执行长度归一化,导致转移到PLDA模型上产生不良的扭曲.实验结果表明,该方法得到和长度归一化相似的效果,部分效果要优于长度归一化.%Recently,speaker verification based on i-vector/PLDA has become the state-of-the-art technique in speaker recognition.For the indefinite time speech,uncertainty of distortion and scaling,when i-vector with length normalization is converted to PLDA model,it affects the recognition rate.In this paper,the normalization of the length of the i-vector on the PLDA model is replaced by the normalization of total variability matrix T,to avoid the poor distortions.Experiments show that the method is similar to the length normalization,some of the results are better than that of the length normalization.
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