论文标题
Interspeech的NPU扬声器验证系统2020远场扬声器验证挑战
NPU Speaker Verification System for INTERSPEECH 2020 Far-Field Speaker Verification Challenge
论文作者
论文摘要
本文介绍了提交给Interspeech 2020远场扬声器验证挑战（FFSVC）的NPU系统。我们特别关注来自单个（任务1）和多个麦克风阵列（Task3）的远场文本依赖性SV。在这种情况下，主要的挑战是简短的话语，跨渠道以及距离不匹配的入学和测试不匹配。相信更好的说话者可以减轻简短话语的影响，我们介绍了一个新的扬声器嵌入架构-Resnet -bam，将瓶颈的注意模块与Resnet集成在一起，作为一种简单有效的方法，以进一步提高重新安置的表示能力。这项贡献可使EER减少多达1％。我们进一步解决了三个方向的不匹配问题。首先，旨在学习域不变特征的域对抗训练可以减少0.8％。其次，包括WPE和波束形成在内的前端信号处理没有明显的贡献，但是与数据选择和域对抗训练一起，可以进一步降低0.5％的EER。最后，与特殊设计的数据选择策略一起使用的数据增强可以导致2％的EER减少。与上述贡献一起，在中间挑战结果中，我们的单个提交系统（无多系统融合）分别在任务1和任务3上获得了第一名和第二名。
This paper describes the NPU system submitted to Interspeech 2020 Far-Field Speaker Verification Challenge (FFSVC). We particularly focus on far-field text-dependent SV from single (task1) and multiple microphone arrays (task3). The major challenges in such scenarios are short utterance and cross-channel and distance mismatch for enrollment and test. With the belief that better speaker embedding can alleviate the effects from short utterance, we introduce a new speaker embedding architecture - ResNet-BAM, which integrates a bottleneck attention module with ResNet as a simple and efficient way to further improve the representation power of ResNet. This contribution brings up to 1% EER reduction. We further address the mismatch problem in three directions. First, domain adversarial training, which aims to learn domain-invariant features, can yield to 0.8% EER reduction. Second, front-end signal processing, including WPE and beamforming, has no obvious contribution, but together with data selection and domain adversarial training, can further contribute to 0.5% EER reduction. Finally, data augmentation, which works with a specifically-designed data selection strategy, can lead to 2% EER reduction. Together with the above contributions, in the middle challenge results, our single submission system (without multi-system fusion) achieves the first and second place on task 1 and task 3, respectively.
