Personalized Text Reconstruction System for Subtitle Adaptation
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Solution Overview
Problem
Conventional systems for providing subtitles and closed captions struggle to effectively address the challenges of readability, understanding, and user experience, particularly for users with low reading skills, unfamiliar vocabulary, and varying language proficiency.
Innovation Solution
A system and method for delivering personalized subtitles and closed captions, which involves receiving textual data and voice input signals, converting them into input text data, and using a text classifier to generate tokens and create a user profile. This profile is used to reconstruct primary text data into personalized text data, tailored to the user's understanding and preferences.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If conventional subtitles are provided with original vocabulary and complex sentences, then the subtitles accurately represent the multimedia content, but users with low reading skills or unfamiliar vocabulary find it difficult to read and understand
Solution Approach 1:
The system dynamically changes parameters of the subtitle text including vocabulary difficulty, sentence complexity, and reading speed based on the user's reading skill level. For users with low reading skills, the system substitutes complex words with simpler alternatives and breaks down long sentences into shorter segments, while maintaining the original meaning and context of the multimedia content.
Solution Approach 2:
The system performs preliminary analysis of the user's reading skills before providing subtitles. It assesses the user's vocabulary level and reading speed preferences in advance, then pre-processes the subtitle text to match the user's capabilities before display, ensuring optimal readability without requiring real-time adjustments during viewing.
2Loss of information
If subtitles contain long sentences with complex vocabulary and idioms, then the subtitles comprehensively convey the dialogue meaning, but the reading time required exceeds the duration of the multimedia content
Solution Approach 1:
The system segments long complex sentences into shorter, more manageable subtitle lines that can be read within the duration of the corresponding video segment. It divides comprehensive dialogue meaning into multiple sequential subtitle cards, each containing a portion of the original meaning in simplified language that can be processed within the available time window.
Solution Approach 2:
The system provides slightly more subtitle lines than the minimum required to convey the dialogue meaning, breaking down complex information into smaller chunks. This excessive segmentation ensures that even users with slower reading speeds can process the information without feeling rushed, while the system prioritizes displaying the most critical information first.
3Loss of information
If subtitles use unfamiliar vocabulary, idioms, and phrases, then the subtitles maintain the original language nuance and cultural context, but users with limited language proficiency cannot understand the subtitles
Solution Approach 1:
The system applies local quality modification by selectively replacing only the difficult vocabulary words and idiomatic expressions while preserving the overall sentence structure and cultural context. It identifies specific problematic words locally within each sentence and substitutes them with simpler alternatives, maintaining the original language nuance where possible while improving comprehensibility for users with limited proficiency.
Solution Approach 2:
The system dynamically adjusts the level of vocabulary simplification and cultural context preservation based on the user's language proficiency level. For users with higher proficiency, it maintains more original language nuance and cultural references, while for users with lower proficiency, it provides greater simplification with optional explanations for culturally specific terms and idioms.
4Adaptability or versatility
If multiple users with different language proficiency levels view the same multimedia content simultaneously, then each user can customize their subtitle preferences, but the system complexity increases to manage multiple user profiles
Solution Approach 1:
The system implements self-service functionality where each user independently configures their own reading skill level, vocabulary preferences, and subtitle parameters through simple interaction. The system automatically creates and manages individual user profiles based on these preferences, eliminating the need for complex centralized profile management while enabling full personalization for multiple users viewing content simultaneously.
Data Source
AI summary
A text reconstruction system for reconstructing a primary text data is provided. A voice input signal of a user is converted into an input text data by a speech recognition module. A text classifier module generates one or more tokens and adds the tokens into a word bag corresponding to the user. A text identifier module generates a text corpus based on the input text data. A user profile builder module creates a user profile based on the word bag, the input text data, and the text corpus. A decision module determines, based on the word bag, whether the primary data is to be reconstructed and reconstructs the primary text data to generate a personalized text data based on the user profile.