论文标题
EXARN：目标扬声器提取的自我负责RNN
ExARN: self-attending RNN for target speaker extraction
论文作者
论文摘要
目标发言人提取是在与其他竞争演讲者的环境中提取由注册话语指定的目标扬声器。因此，任务需要在同一时间解决两个问题，即说话者的识别和分离。在本文中，我们结合了自我注意事项和复发性神经网络（RNN）。此外，我们利用各种方式将不同的辅助信息与混合表示形式相结合。实验结果表明，我们提出的模型在目标扬声器提取的任务上实现了出色的性能。
Target speaker extraction is to extract the target speaker, specified by enrollment utterance, in an environment with other competing speakers. Therefore, the task needs to solve two problems, speaker identification and separation, at the same time. In this paper, we combine self-attention and Recurrent Neural Networks (RNN). Further, we exploit various ways to combining different auxiliary information with mixed representations. Experimental results show that our proposed model achieves excellent performance on the task of target speaker extraction.
