Sign Language Translation via Emphasis-Based Rephrasing

The system segments the translation process into multiple stages: acoustic feature extraction, emphasis score prediction for each word, sentence rephrasing based on emphasis patterns, and sign language character generation. This segmentation allows the system to capture nuanced intent while managing complexity through modular processing steps.

The patent introduces an intermediary rephrasing stage that transforms the original sentence into an emphasized version before converting to sign language. This intermediary step acts as a mediator that preserves the original meaning while highlighting key intent, enabling more accurate sign language translation without directly complicating the core translation mechanism.

The system enables self-service automatic translation by equipping electronic devices with acoustic analysis and natural language processing capabilities. The device independently extracts acoustic features, predicts emphasis scores, rephrases sentences, and generates sign language characters without requiring human interpreter intervention, making the service accessible and cost-effective while maintaining reliability through automated accuracy.

The patent replaces the mechanical system of human interpreters with an automated electronic system that uses acoustic feature extraction, machine learning-based emphasis prediction, and algorithmic sentence rephrasing. This substitution eliminates the need for human physical presence while maintaining translation accuracy through computational analysis of acoustic patterns and linguistic structures.