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Knowing the non-target speakers: The effect of the i-vector population for PLDA training in speaker recognition

机译:了解非目标说话人:i-vector种群对说话人识别中PLDA训练的影响

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Inspired by the NIST SRE-2012 evaluation conditions we train the PLDA classifier in an i-vector speaker recognition system with different speaker populations, either including or excluding the target speakers in the evaluation. Including the target speakers in the PLDA training is always beneficial w.r.t. completely excluding them—which is the normal situation in pre-2012 SRE protocols—even in the Pknown = 0 evaluation condition. However, adding other speakers than just the targets speakers can slightly increase performance. We also investigated the effect of adding i-vectors extracted from segments with added noise in the PLDA training. This generally makes the system more robust to noise in the test segments, and doesn't hurt performance in the clean condition. The paper further details the 'simple to compound' log-likelihood-ratio conversion necessary for SRE-2012 style calibration.
机译:受NIST SRE-2012评估条件的启发,我们在i-vector说话者识别系统中训练了PLDA分类器,该系统具有不同的说话者群体,包括或不包括评估中的目标说话者。在PLDA培训中包括目标演讲者总是有益的。即使在P = 0评估条件下,也完全排除了它们(这是2012年以前的SRE协议中的正常情况)。但是,添加除目标扬声器之外的其他扬声器可以稍微提高性能。我们还研究了在PLDA训练中添加从段中提取的i-vector以及添加的噪声的效果。通常,这会使系统对测试段中的噪声更加健壮,并且在清洁条件下不会损害性能。本文进一步详细介绍了SRE-2012样式校准所需的“简单到复合”对数似然比转换。

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