论文标题
扬声器验证的无参数细心评分
Parameter-Free Attentive Scoring for Speaker Verification
论文作者
论文摘要
本文介绍了一项针对说话者验证的无参数专注评分的新研究。无参数评分提供了比较扬声器表示的灵活性，而无需随附的参数评分模型。受到变压器神经网络中的注意力成分的启发，我们提出了缩放点产品注意机制的变体，以比较注册和测试段表示。此外，这项工作探讨了（i）不同类型的归一化的性能的影响，（ii）独立与绑定查询/密钥估计，（iii）改变键值对的数量，以及（iv）汇总多个注册话语统计量。 4任务平均值的实验结果表明，与最佳余弦相似性基线相比，简单的无参数评分机制可以提高平均EER 10％。
This paper presents a novel study of parameter-free attentive scoring for speaker verification. Parameter-free scoring provides the flexibility of comparing speaker representations without the need of an accompanying parametric scoring model. Inspired by the attention component in Transformer neural networks, we propose a variant of the scaled dot product attention mechanism to compare enrollment and test segment representations. In addition, this work explores the effect on performance of (i) different types of normalization, (ii) independent versus tied query/key estimation, (iii) varying the number of key-value pairs and (iv) pooling multiple enrollment utterance statistics. Experimental results for a 4 task average show that a simple parameter-free attentive scoring mechanism can improve the average EER by 10% over the best cosine similarity baseline.
