BeamTransformer：基于麦克风阵列的重叠语音检测
BeamTransformer: Microphone Array-based Overlapping Speech Detection
摘要：
我们提出了波束Transformer，一个有效的架构，利用波束形成器的空间滤波和Transformer的上下文序列建模的能力的边缘。BeamTransformer旨在优化来自不同空间方向的信号之间的时序关系的建模。重叠语音检测是这样的优化是有利的任务之一。在本文中，我们有效地应用BeamTransformer检测重叠段。与单通道方法相比，BeamTransformer在学习识别不同波束序列之间的关系方面表现出色，因此不仅能够从声学信号中进行预测，还能够定位源。结果表明，麦克风阵列信号的成功合并可以导致显着的增益。此外，BeamTransformer更进一步，因为来自重叠扬声器的语音已在内部分离为不同的波束。
英文摘要：
We propose BeamTransformer, an efficient architecture to leverage beamformer's edge in spatial filtering and transformer's capability in context sequence modeling. BeamTransformer seeks to optimize modeling of sequential relationship among signals from different spatial direction. Overlapping speech detection is one of the tasks where such optimization is favorable. In this paper we effectively apply BeamTransformer to detect overlapping segments. Comparing to single-channel approach, BeamTransformer exceeds in learning to identify the relationship among different beam sequences and hence able to make predictions not only from the acoustic signals but also the localization of the source. The results indicate that a successful incorporation of microphone array signals can lead to remarkable gains. Moreover, BeamTransformer takes one step further, as speech from overlapped speakers have been internally separated into different beams.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
