Dynamic Grammar Builder for Automated Closed Captioning
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Solution Overview
Problem
Human-based closed captioning for live media content is inefficient and prone to errors due to the difficulty in keeping up with fast-paced audio, especially in live broadcasts, and is costly, while existing automated systems struggle with accurately identifying named entities with varying spellings or new terms.
Innovation Solution
An automated closed captioning system that uses a dynamic grammar builder to generate a list of named entities from real-time social network data, rank them based on trending occurrences, and apply this dynamic grammar to improve speech recognition accuracy in creating closed caption text for media content.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If human typists are employed to create closed captioning text, then the accuracy of closed captioning can be maintained, but the cost and time consumption increase significantly
Solution Approach 1:
The system enables automated closed captioning where the speech recognition component processes audio content independently to generate caption text, eliminating the need for human typists to manually transcribe. The dynamic grammar builder continuously updates the grammar based on real-time content, allowing the system to self-improve accuracy while maintaining high productivity.
Solution Approach 2:
The dynamic grammar builder monitors real-time content and trending named entities, then feeds this information back to update the grammar used by the speech recognition component. This continuous feedback loop improves recognition accuracy over time without requiring human intervention, resolving the contradiction between accuracy and efficiency.
2Productivity
If multiple humans are employed to handle live broadcast captioning, then the productivity increases, but the cost and coordination complexity increase
Solution Approach 1:
The automated system performs all captioning tasks independently through the speech recognition component, which processes audio and generates captions without human intervention. The dynamic grammar builder automatically updates the recognition grammar based on real-time content analysis, eliminating the need for multiple human operators and the coordination complexity that would ensue.
Solution Approach 2:
The system dynamically adjusts the grammar parameters used by speech recognition based on real-time analysis of trending named entities and content. This adaptive parameter change allows a single automated system to handle diverse content types and topics effectively, replacing multiple specialized human typists without the coordination overhead.
3Productivity
If existing automated speech recognition systems are used, then productivity increases, but the accuracy of named entity recognition decreases
Solution Approach 1:
The grammar used by the speech recognition component is not static but dynamically updated by the dynamic grammar builder in real-time. The system continuously analyzes trending named entities from the content and adjusts the grammar accordingly, allowing the automated system to maintain high named entity recognition accuracy while preserving the productivity benefits of automation.
Solution Approach 2:
The dynamic grammar builder performs preliminary analysis of content and identifies trending named entities before they appear in the speech stream. This preliminary action allows the speech recognition system to have accurate grammar rules ready in advance, improving named entity recognition accuracy without compromising the speed of automated captioning.
Data Source
AI summary
One or more systems and/or techniques are provided for automatic closed captioning for media content. In an example, real-time content, occurring within a threshold timespan of a broadcast of media content (e.g., social network posts occurring during and an hour before a live broadcast of an interview), may be accessed. A list of named entities, occurring within the social network data, may be generated (e.g., Interviewer Jon, Interviewee Kathy, Husband Dave, Son Jack, etc.). A ranked list of named entities may be created based upon trending named entities within the list of named entities (e.g., a named entity may be ranked higher based upon a more frequent occurrence within the social network posts). A dynamic grammar (e.g., library, etc.) may be built based upon the ranked list of named entities. Speech recognition may be performed upon the broadcast of media content utilizing the dynamic grammar to create closed caption text.