Abstract
A multi-modal contextual search system is proposed herein that may revolutionize multimedia content retrieval in collaborative environments. Unlike traditional keyword-based searches, the proposed system employs advanced artificial intelligence (AI) techniques to directly search within audio and video files in order to retrieve content, overcoming transcription limitations. By leveraging neural-encoder inspired embedding systems and AI-powered audio search without transcription, the proposed system ensures swift and accurate access to crucial information, enhancing productivity and knowledge dissemination. With enhanced features such as audio metadata extraction and semantic matching, the innovative solution proposed herein offers a comprehensive and contextually relevant search experience tailored for enterprise needs.
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 License.
Recommended Citation
Sabra, Adam; Gopikrishnan, Rekha; Bisht, Altanai; Dahir, Hazim; Babu, Karthik Babu Harichandra; and Kini, Savita, "MULTI-MODAL CONTEXTUAL SEARCH FOR ENTERPRISE CONTENT", Technical Disclosure Commons, (October 27, 2024)
https://www.tdcommons.org/dpubs_series/7479