  While speech interaction finds widespread utility within the Extended Reality
(XR) domain, conventional vocal speech keyword spotting systems continue to
grapple with formidable challenges, including suboptimal performance in noisy
environments, impracticality in situations requiring silence, and
susceptibility to inadvertent activations when others speak nearby. These
challenges, however, can potentially be surmounted through the cost-effective
fusion of voice and lip movement information. Consequently, we propose a novel
vocal-echoic dual-modal keyword spotting system designed for XR headsets. We
devise two different modal fusion approches and conduct experiments to test the
system's performance across diverse scenarios. The results show that our
dual-modal system not only consistently outperforms its single-modal
counterparts, demonstrating higher precision in both typical and noisy
environments, but also excels in accurately identifying silent utterances.
Furthermore, we have successfully applied the system in real-time
demonstrations, achieving promising results. The code is available at
https://github.com/caizhuojiang/VE-KWS.
