Accessibility Content Rendering With AI-Generated Sign Language Tracks
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Solution Overview
Problem
Existing accessibility solutions for individuals with disabilities, such as sign language translation, are expensive, inconvenient, and lack the ability to capture emotional intensity and emphasis, while analogous challenges exist for vision-compensated and neurodiversity-sensitive content.
Innovation Solution
A system and method utilizing machine learning models to automate the rendering of accessibility-enhanced content, including sign language performance, synchronized with primary content, using video tokens and emotive data sets to dynamically generate facial expressions and gestures, and optionally incorporating haptic effects.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If human sign language translators are used, then emotional intensity and emphasis can be captured, but cost increases and scalability is limited
Solution Approach 1:
The patent uses AI models to copy and replicate the performance patterns of human sign language translators. The system trains on video data of human translators to create synthetic sign language performances that replicate emotional intensity and emphasis without requiring actual human translators for each content piece, thereby achieving scalability while maintaining quality.
Solution Approach 2:
The patent replaces the mechanical system of human translators with an automated AI-based system. The AI model processes audio content and generates corresponding sign language video outputs automatically, eliminating the need for manual human intervention in the translation process and enabling unlimited scalability.
2Reliability
If human sign language translators are used, then translation quality is high, but cost and time consumption increase
Solution Approach 1:
The patent performs preliminary training of the AI model using a comprehensive dataset of sign language performances. Once trained, the model can rapidly generate translations for new content without requiring time-consuming manual translation processes, thus reducing time consumption while maintaining high translation quality through the pre-learned patterns.
Solution Approach 2:
The patent substitutes the time-consuming manual translation process with an automated AI system that processes content instantly. The AI model analyzes audio input and generates synchronized sign language video outputs in real-time, dramatically reducing the time required compared to human translators.
3Device complexity
If hand signals alone are used for sign language translation, then simplicity is maintained, but emotional intensity and emphasis are lost
Solution Approach 1:
The patent applies dynamic variations in the sign language performance to convey emotional intensity and emphasis. The AI model adjusts factors such as speed, force, facial expressions, and body movements in real-time based on the emotional content of the audio, transforming static hand signals into dynamic, emotionally expressive performances that accurately reflect the source material.
Solution Approach 2:
The patent changes multiple parameters of the sign language performance including speed, force, facial expressions, and body movements to convey emotional intensity. The AI model modulates these parameters dynamically based on the emotional content, allowing the same basic sign to be performed with varying intensity and emphasis to match the original audio's emotional tone.
Data Source
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AI summary
A user system for rendering accessibility enhanced content includes processing hardware, a display, and a memory storing software code. The processing hardware executes the software code to receive primary content from a content distributor and determine whether the primary content is accessibility enhanced content including an accessibility track. When the primary content omits the accessibility track, the processing hardware executes the software code to perform a visual analysis, an audio analysis, or both, of the primary content, generate, based on the visual analysis and/or the audio analysis, the accessibility track to include at least one of a sign language performance or one or more video tokens configured to be played back during playback of the primary content, and synchronize the accessibility track to the primary content. The processing hardware also executes the software code to render, using the display, the primary content or the accessibility enhanced content.