Video Caption Augmentation Using Time-Coded Transcript Enhancements
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Solution Overview
Problem
Existing video content enhancement methods, particularly for adding captions, are labor-intensive and often require special training, and automated solutions like speech-to-text voice recognition produce unsuitable outputs, lacking user control and accuracy.
Innovation Solution
A system comprising an augmentation generator and applicator that processes input video to generate transcript-derived enhancements, including captions, with precise timing and positioning, and optionally translates and synchronizes them with the video content.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If speech-to-text voice recognition is used to automate video content enhancement, then automation extent is improved, but output quality deteriorates
Solution Approach 1:
The patent introduces an intermediary human editor who receives automated speech-to-text output and refines it into accurate captions. This mediator figure bridges the gap between automated processing and quality requirements, allowing the system to maintain high automation while achieving suitable output quality through human-in-the-loop verification and correction.
Solution Approach 2:
The system implements feedback mechanisms where caption quality is evaluated and used to improve the automated transcription process. User corrections and quality metrics feed back into the system to refine future automated captioning, gradually improving the accuracy of speech-to-text conversion while maintaining high automation levels.
2Manufacturing precision
If manual captioning is performed to ensure high quality output, then manufacturing precision is improved, but productivity deteriorates
Solution Approach 1:
The captioning process is segmented into distinct phases: automated speech-to-text generation handles the bulk of caption creation, while human editors only intervene for verification and correction of specific segments. This segmentation allows most content to be processed automatically (maintaining high productivity) while ensuring quality through targeted human review (maintaining manufacturing precision).
Solution Approach 2:
The system performs preliminary automated transcription before human editing is applied. By pre-generating captions through speech-to-text technology, the system establishes a baseline that requires minimal human intervention, thereby maintaining high productivity while still achieving quality output through subsequent refinement.
3Manufacturing precision
If manual captioning is performed for live broadcasts, then manufacturing precision is improved, but loss of time increases
Solution Approach 1:
For live broadcasts, the system performs preliminary automated transcription in real-time as the audio is captured. This preliminary action generates captions immediately without waiting for post-production processing, enabling near-real-time caption delivery while maintaining accuracy through continuous speech recognition and minimal human verification.
Solution Approach 2:
The automated speech-to-text system operates continuously during live broadcasts, providing uninterrupted caption generation as audio is captured. This continuous processing eliminates the time delays associated with batch processing or post-production captioning, while quality control is maintained through ongoing human monitoring and correction of live output.
Data Source
AI summary
Video content enhancement methods, systems and devices are disclosed. An input video 11 is processed to generate an augmentation descriptor 4 from it, the augmentation descriptor 4 defining augmentations that enhance the content of the input video 11. An output video 14 is generated by combining the augmentations defined by the augmentation descriptor 4 with the input video 11, so that playback of the output video 14 features said augmentations. A timecoded transcript is generated from at least an audio portion of the input video 11, and the timecoded transcript is processed to generate from it a set of transcript-derived enhancements. The set of transcript-derived enhancements are added to the augmentation descriptor 4 as augmentations for enhancing the content of the input video 11.