Speech-to-Text Captioning With Confidence-Based Review
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Solution Overview
Problem
Traditional automatic speech-to-text frameworks for video content lack accuracy and user control over captions, particularly in scenarios with burned-in captions and background music, and fail to differentiate between high and low accuracy captions.
Innovation Solution
A framework that automatically generates captions, determines their accuracy using token-level confidence values and silence intervals, and prompts users for manual review of low-accuracy captions, while detecting burned-in captions and allowing user settings for caption control.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If automated speech-to-text frameworks are used for video content, then caption generation is automated, but caption accuracy deteriorates particularly in scenarios with burned-in captions and background music
Solution Approach 1:
The system segments the caption generation process into multiple stages: automatic caption generation, quality assessment using multiple criteria (clarity, completeness, accuracy), and conditional routing to manual review or direct publication. This segmentation allows automated processing while maintaining quality control through staged evaluation.
Solution Approach 2:
The system implements feedback loops where generated captions are assessed against quality metrics, and low-quality captions trigger feedback to users for manual review and correction. The system continuously learns from user corrections to improve future automatic caption generation accuracy.
2Device complexity
If traditional speech-to-text frameworks are used, then caption generation is simple, but user control over captions deteriorates
Solution Approach 1:
The system dynamically adjusts the level of user involvement based on caption quality assessment. High-quality captions are published automatically without user intervention, while low-quality captions trigger user review workflows. Users can also manually adjust quality thresholds and control their preferred level of involvement, making the system adaptable to different user needs.
3Productivity
If all captions are automatically published, then productivity is improved, but caption quality deteriorates due to lack of review
Solution Approach 1:
The system changes the parameter of quality assessment from binary (publish/not publish) to a continuous scale with multiple quality dimensions. Captions are evaluated on clarity, completeness, and accuracy, with dynamic quality thresholds that can be adjusted. This allows the system to publish captions efficiently when quality standards are met while maintaining reliability through configurable quality gates.
4Reliability
If manual review is required for all captions, then caption quality is improved, but productivity deteriorates due to increased user workload
Solution Approach 1:
The system applies partial manual review action only when necessary. Instead of requiring review for all captions, the system generates captions automatically and only triggers manual review for those that fail quality assessment thresholds. This partial application of manual review maintains quality for problematic captions while preserving productivity for high-quality automatic captions.
Data Source
AI summary
The disclosed systems and methods may include (1) generating a caption for a spoken phrase in a video, (2) determining an accuracy rating for the caption, and (3) in response to determining that the accuracy rating is below an accuracy threshold, prompting a user to manually review the caption prior to publishing the caption. Various other methods, systems, and computer-readable media are also disclosed.