Variable-Length Video Captions for Accessible Playback Control
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Solution Overview
Problem
Conventional video chat systems fail to effectively address communication difficulties for individuals with hearing and vision impairments, particularly those using hearing aids and reading glasses, due to slower processing speeds and inadequate captioning solutions.
Innovation Solution
A machine learning-based system utilizing neural networks for audio segmentation and caption generation, allowing users to customize playback options, including variable length captions, to enhance understanding and accessibility.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional video chat systems are used, then basic communication is possible, but users with hearing and vision impairments experience communication difficulties due to slower processing speeds and inadequate captioning solutions
Solution Approach 1:
The patent segments audio content into discrete units and generates captions at variable lengths (short, medium, long) to match different user processing speeds. This segmentation allows users with hearing and vision impairments to consume information at their own pace, resolving the contradiction between communication reliability and processing speed by providing structured, manageable information units.
Solution Approach 2:
The system changes the parameter of caption length to accommodate different user needs. By providing variable length captions rather than fixed-length captions, the system adapts to users with slower processing speeds while maintaining communication effectiveness, directly addressing the technical contradiction between reliability and ease of operation.
2Adaptability or versatility
If variable length captions are generated using machine learning, then user understanding and accessibility are enhanced, but system complexity increases
Solution Approach 1:
The patent introduces machine learning models as intermediaries between the audio input and caption output. These models automatically generate variable length captions based on user preferences, providing adaptability without requiring complex manual processing. The intermediary handles the complexity internally while presenting a simplified interface to users, resolving the contradiction between adaptability and device complexity.
3Productivity
If automated speech-to-text algorithms are used, then caption generation is efficient, but the response speed is slower than user expectations and processing effort is excessive
Solution Approach 1:
The system implements partial action by generating captions at different length levels (short, medium, long) based on user needs. Instead of generating complete verbatim transcripts, the system can provide summarized captions that capture essential information faster, reducing processing time while maintaining productivity. This partial action approach resolves the contradiction between caption generation efficiency and processing time loss.
Data Source
AI summary
Techniques are described herein for providing variable length text during playback of a video. Audio corresponding to a portion of the video may be identified and segmented by a neural network. A number of captions of varying length can be identified for each segment using a second neural network. The user may be presented with a user interface that enables playback of various portions of the video (e.g., options corresponding to previously uttered sentences). During playback, one of the captions may be provided. Which caption is presented may be controlled by the user. At least some of these captions can be shorter than the actual words spoken during the segment, while still providing the same or substantially similar semantic meaning.