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In an increasingly demanding media consumption landscape, broadcasters can utilize automatic speech recognition (ASR) to overcome burdensome tasks, extract valuable information and gain a deeper understanding of their programming. This AI-driven technology uses advanced machine learning algorithms and deep neural networks. This enables higher accuracy in transcribing spoken content and vocabulary customization while enhancing real-time monitoring features and data analytics procedures.
By continuously transcribing aired content and linking frame-accurate media events to AI-generated textual metadata, broadcasters can optimize resources required for content creation, review, indexing, and sharing, while being swift to respond to regulatory compliance inquiries. This content classification network facilitates an effective “new content discovery,” as it empowers newsroom teams to find trending news and streamline their clipping workflows. Other departments may use similar features to generate data-driven reports for compliance, ads, royalties, and rating performance.
Leveraging this technology is transformative in terms of automation, reducing costs, and mitigating risks of non-compliance — effectively future-proofing your organization.
Spotting trends, simplifying compliance
In addition to a full-text transcription and the extraction of closed captioning, the AI algorithms can detect recurring topics, keywords, and themes across the newscast or other programs. By merging this textual information with ratings, it’s possible to automatically categorize, tag and segment the content according to performance or subject. This trend analysis provides valuable insights into audience preferences, content performance, and emerging patterns, informing programming decisions and compliance monitoring.