Keyword extraction-based audio and video recognition technology in mobile office environment

This paper explores the integration of keyword extraction techniques into audio and video recognition systems designed for mobile office environments. As the prevalence of mobile devices reshapes traditional workspaces, the demand for advanced technologies that can efficiently process and analyze audio and video data in real time has increased significantly. This research examines the unique challenges presented by dynamic mobile contexts, such as fluctuating background noise, varying audio quality, and the mobility of devices, which complicate traditional recognition methods. To address these issues, a novel framework is proposed that combines both machine learning and deep learning techniques to enhance the accuracy and robustness of audio and video recognition in such environments. Experimental validation of the proposed model demonstrates a high accuracy of 92% in keyword detection, confirming its effectiveness in real-world applications. The results highlight the potential of this framework to not only improve recognition performance but also to create more adaptive, context-aware systems capable of optimizing user experience in mobile office settings. This study contributes to the development of smarter, more efficient recognition systems that are better suited to the evolving demands of modern workspaces.