On-Screen Caption Text Error Correction via Knowledge Graph Context

The patent introduces an intermediary correction system that sits between the ASR output and final caption display. This intermediary layer uses multiple correction techniques including pronunciation dictionaries, context analysis, and alternative word matching to fix ASR errors without reducing overall productivity. The system automatically identifies and corrects errors in terms of art, foreign names, and homophones while maintaining high-speed caption generation.

The patent replaces manual stenographer correction with an automated electronic correction system. Instead of relying on human expertise to manually fix ASR errors, the system uses computational methods including pronunciation pattern matching, contextual analysis, and database lookups to automatically substitute correct terms for erroneous ASR output, thereby maintaining productivity while improving precision.

The patent implements a self-service correction system where the captioning system automatically identifies and corrects its own errors without human intervention. The system uses pronunciation dictionaries, contextual analysis, and alternative word matching algorithms to self-correct ASR errors in real-time, eliminating the need for manual stenographer review while maintaining high accuracy and productivity.

The patent incorporates feedback mechanisms where the system continuously monitors caption text for errors using multiple validation techniques. When errors are detected, the system automatically retrieves alternative candidates from pronunciation dictionaries and context databases, evaluates them against the original speech context, and implements corrections. This closed-loop feedback system maintains high precision without sacrificing productivity.

The patent segments the vocabulary knowledge into multiple specialized dictionaries organized by domain and language. Instead of one massive dictionary, the system uses separate pronunciation dictionaries for different languages (English, Spanish, French, German), specialized dictionaries for terms of art and jargon, and context-specific vocabularies. This segmentation improves recognition accuracy for specialized terms while keeping each individual dictionary manageable in size and complexity.

The patent creates a universal correction framework that handles multiple languages, domains, and error types through a single integrated system. The pronunciation dictionary system is designed to be language-agnostic and domain-agnostic, using universal phonetic algorithms and contextual analysis techniques that work across different languages and specialized fields. This multi-functional approach improves precision for diverse terms without proportionally increasing system complexity.