Leveraging AI/ML for Multilingual Content Integrity

Broadcast Engineering and IT Conference , Core Education Collection , Core Education Collection: Connect Series

Originally Aired - Tuesday, April 16 | 11:30 AM - 11:50 AM PT

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In today's fast-paced world of international broadcasting, the provisioning of multilingual content, particularly for live sports and news, is a critical service enabler to fit diverse audiences.

Yet, the quality of language services, such as subtitling, translation, and transcription, is often undermined by inaccuracies due to translator expertise, ambient noise, and speaker accents. In all cases, providing the service isn’t enough, confidence that the results are correct in all supported languages is important in ensuring viewer uptake and retention in their respective markets. Low quality translation / transcription, intermittent outages, content errors and similar all send a negative message to the viewer that causes a disconnect from the content. The key to managing confidence in such environments is the use of a monitoring system that can monitor across any instance of content, and use best-in-class continuously improving AIML web services to provide an integrated solution.

This presentation will outline the problem set to be solved in technical and commercial terms. The discussion will highlight a comprehensive, AIML-centric software approach to these industry challenges, showcasing a monitoring system that employs automatic speech recognition and natural language processing to ensure real-time linguistic integrity and enhanced quality of experience.

The session will provide an in-depth examination of the system's architecture, emphasizing real-time language detection and the application of self-improving AIML models trained on specialized datasets. Special attention will be paid to how broadcasters can leverage their existing content repositories to consistently improve the accuracy of the system and contribute to the refinement of customized language models to fit wider arrays of use-cases.

Moreover, the presentation will explore the innovative use of AIML for visual data verification and the automatic collection of metadata, thus expanding the monitoring system's capabilities. By connecting this system with media asset management (MAM) systems, the presentation will help the audience understand how advanced metadata extraction from transcriptions and audio/video analysis contributes to a content searching and cataloging that provides a better broadcaster user experience.

Attendees will be presented with a live demonstration of the system's operational strengths, underscoring its scalability, adaptability, and advanced monitoring capabilities that serve both the broadcast users and consumer viewers.

In conclusion, the presentation will underscore the transformative impact of AIML on multilingual broadcasting, establishing a new paradigm for global content standards and elevating monitoring to unprecedented levels of effectiveness.