podscoped

Podscoped is a term used in media technology to describe a framework and set of tools for analyzing, organizing, and retrieving podcast content across platforms. It encompasses data models, processing pipelines, and user-facing interfaces aimed at adding context to podcast episodes, such as transcripts, topics, speakers, and scene-level summaries.

Core components include transcript generation (speech-to-text), speaker diarization, time-stamped tagging of topics and entities, metadata extraction from show notes and RSS feeds, and a full-text search across transcripts. Additional features often include sentiment analysis, content recommendations, accessibility options, and API access for publishers and developers.

Data sources typically include syndicated podcast feeds, hosted audio files, and user-uploaded content. Processing pipelines cover transcription, entity recognition, tagging, indexing, and enrichment. Privacy and governance considerations are addressed through data minimization, opt-in analytics, and compliance with privacy laws.

Applications span podcast platforms seeking improved search and discovery, publishers aiming to repurpose content and enhance show notes, advertisers evaluating targeted placements, and researchers studying listening behaviors. Podscoped-style capabilities also support accessibility for the deaf and hard of hearing communities through accurate transcripts and summaries.

While there is no single official standard, the term podscoped appears in industry discussions as a descriptor for contextualized podcast data and interoperable analytics. See also podcast analytics, transcript search, and speaker diarization.