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Improving PLDA speaker verification performance using domain mismatch compensation techniques

机译：使用域失配补偿技术提高PLDA扬声器验证性能

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摘要

The performance of state-of-the-art i-vector speaker verification systems relies on a large amount of training data for probabilistic linear discriminant analysis (PLDA) modeling. During the evaluation, it is also crucial that the target condition data is matched well with the development data used for PLDA training. However, in many practical scenarios, these systems have to be developed, and trained, using data that is often outside the domain of the intended application, since the collection of a significant amount of in-domain data is often difficult. Experimental studies have found that PLDA speaker verification performance degrades significantly due to this development/evaluation mismatch. This paper introduces a domain-invariant linear discriminant analysis (DI-LDA) technique for out-domain PLDA speaker verification that compensates domain mismatch in the LDA sub-space. We also propose a domain-invariant probabilistic linear discriminant analysis (DI-PLDA) technique for domain mismatch modeling in the PLDA subspace, using only a small amount of in-domain data. In addition, we propose the sequential and score-level combination of DI-LDA, and DI-PLDA to further improve out-domain speaker verification performance. Experimental results show the proposed domain mismatch compensation techniques yield at least 27% and 14.5% improvement in equal error rate (EER) over a pooled PLDA system for telephone-telephone and interview-interview conditions, respectively. Finally, we show that the improvement over the baseline pooled system can be attained even when significantly reducing the number of in-domain speakers, down to 30 in most of the evaluation conditions.
机译：最新的i-vector说话者验证系统的性能依赖于大量的训练数据来进行概率线性判别分析（PLDA）建模。在评估期间，将目标条件数据与用于PLDA培训的开发数据进行良好匹配也至关重要。但是，在许多实际情况下，必须使用经常在预期应用程序范围之外的数据来开发和培训这些系统，因为通常很难收集大量域内数据。实验研究发现，由于这种开发/评估不匹配，PLDA说话人的验证性能会大大降低。本文介绍了一种用于域外PLDA说话人验证的域不变线性判别分析（DI-LDA）技术，该技术可补偿LDA子空间中的域失配。我们还提出了一种域不变概率线性判别分析（DI-PLDA）技术，用于PLDA子空间中的域失配建模，仅使用少量域内数据。此外，我们提出了DI-LDA和DI-PLDA的顺序和分数级别组合，以进一步提高域外说话者验证性能。实验结果表明，所提出的域失配补偿技术分别比电话电话和面试采访条件下的混合PLDA系统的均等错误率（EER）至少提高了27％和14.5％。最后，我们表明，即使大大减少了域内发言人的数量（在大多数评估条件下减少到30位），也可以实现对基线合并系统的改进。

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