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.
Enhancing Speech Interaction in Extended Reality with a Vocal-Echoic Dual-Modal Keyword Spotting System
In the field of Extended Reality (XR), speech interaction plays a crucial role in providing a natural and intuitive user experience. However, traditional vocal speech keyword spotting systems face several challenges that hinder their performance and usability in XR environments. These challenges include suboptimal performance in noisy surroundings, impracticality in situations where silence is required, and susceptibility to inadvertent activations when others speak nearby.
To overcome these limitations, a novel solution has been proposed – a vocal-echoic dual-modal keyword spotting system designed specifically for XR headsets. By combining voice and lip movement information, this system aims to achieve more accurate and reliable speech recognition in diverse scenarios.
The multi-disciplinary nature of this concept becomes apparent when we consider the various components involved. On one hand, there is the domain of multimedia information systems, which deals with the processing and analysis of different types of media, including speech and visual data. On the other hand, we have Animations, Artificial Reality, Augmented Reality, and Virtual Realities, all of which contribute to the immersive XR experience.
In this research, two different modal fusion approaches were devised and tested through experiments. The results demonstrate that the vocal-echoic dual-modal system consistently outperforms its single-modal counterparts. It exhibits higher precision in typical and noisy environments while also excelling in accurately identifying silent utterances.
One notable aspect of this system is its real-time applicability. Real-time demonstrations have been successfully conducted, showcasing the system’s potential for practical use. This opens up possibilities for integrating the vocal-echoic dual-modal keyword spotting system into XR applications, enabling more seamless and reliable speech interaction.
The availability of the code on GitHub (https://github.com/caizhuojiang/VE-KWS) further enhances the research’s accessibility and promotes collaboration and further innovation in the field.
In conclusion, the development of a vocal-echoic dual-modal keyword spotting system for XR headsets holds significant promise in enhancing speech interaction within Extended Reality. The fusion of voice and lip movement information addresses the challenges faced by traditional vocal speech keyword spotting systems, leading to improved performance and usability. As the field of XR continues to evolve, advancements in multimedia information systems, animations, artificial reality, augmented reality, and virtual realities will play a crucial role in shaping the future of immersive user experiences.