论文标题
短期扬声器验证挑战2020的UIAI系统
UIAI System for Short-Duration Speaker Verification Challenge 2020
论文作者
论文摘要
在这项工作中，我们介绍了2020年短期扬声器验证（SDSV）挑战的UIAI条目的系统描述。我们的重点是专门针对文本依赖的说话者验证的任务1。我们研究了自动扬声器验证（ASV）和话语验证（UV）的不同特征提取和建模方法。我们还研究了将UV和ASV模块组合的不同融合策略。我们对挑战的主要提交是七个子系统的融合，这些子系统在评估集中产生了归一化的最低检测成本函数（MIDCF），同等错误率（EER）为2.14％。由基于传递的识别模型组成的单个系统具有电话 - 歧义性瓶颈功能，可提供0.118的归一化MindCF，并且比最先进的挑战基线相对改善了19％。
In this work, we present the system description of the UIAI entry for the short-duration speaker verification (SdSV) challenge 2020. Our focus is on Task 1 dedicated to text-dependent speaker verification. We investigate different feature extraction and modeling approaches for automatic speaker verification (ASV) and utterance verification (UV). We have also studied different fusion strategies for combining UV and ASV modules. Our primary submission to the challenge is the fusion of seven subsystems which yields a normalized minimum detection cost function (minDCF) of 0.072 and an equal error rate (EER) of 2.14% on the evaluation set. The single system consisting of a pass-phrase identification based model with phone-discriminative bottleneck features gives a normalized minDCF of 0.118 and achieves 19% relative improvement over the state-of-the-art challenge baseline.
