Sign Language Generation via NLP Sentence Segmentation
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Solution Overview
Problem
Current technologies face challenges in objectively interpreting and translating spoken sentences into sign language videos, particularly in determining sentence attributes and generating synchronized sign language videos that accurately correspond to spoken sentences.
Innovation Solution
The implementation of natural language processing to extract sentences from speech videos, identify sentence structures, and translate them into sign language, ensuring synchronization by determining sentence start and end times, using a combination of hardware and programming to execute video analysis, sentence structure analysis, sentiment analysis, and sign video generation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If natural language processing is applied to extract and analyze sentences from speech videos, then translation accuracy and synchronization are improved, but device complexity and processing time increase
Solution Approach 1:
The system segments the speech video into individual sentences using natural language processing, analyzing each sentence separately for structure, sentiment, and translation. This segmentation allows for precise translation accuracy while managing complexity by processing discrete units rather than continuous streams.
Solution Approach 2:
The system performs preliminary actions by extracting sentences, determining their start and end times, analyzing sentence structures, and identifying sentiments before the actual translation process. This preliminary analysis improves translation accuracy by preparing structured data in advance, while the modular nature of these preliminary steps helps manage system complexity.
2Manufacturing precision
If sentence structure analysis and sentiment analysis are performed for each sentence, then sign language generation accuracy is improved, but processing duration increases
Solution Approach 1:
The system divides the processing into segmented steps for each sentence: extraction, structure analysis, sentiment analysis, and translation. This segmentation improves generation accuracy by thoroughly analyzing each sentence while managing processing time through efficient, modular operations that can be optimized independently.
Solution Approach 2:
The system changes parameters such as sentence start and end times, structure types, and sentiment values to accurately represent each sentence for sign language generation. By systematically varying and analyzing these parameters, the system achieves high generation accuracy while the automated parameter management helps control processing time.
3Productivity
If automated natural language processing is used to translate speech to sign language, then productivity is improved, but measurement precision of sentence attributes may deteriorate
Solution Approach 1:
The system uses feedback mechanisms where the automated natural language processing analyzes sentence structures and sentiments, then refines its translations based on the determined attributes. This feedback loop maintains high productivity through automation while improving measurement precision of sentence attributes through iterative refinement and validation.
Solution Approach 2:
The system replaces manual translation mechanisms with automated natural language processing that uses computational analysis of sentence structures and sentiments. This substitution maintains high productivity through automation while the sophisticated algorithms ensure accurate measurement of sentence attributes by systematically analyzing linguistic features.
Data Source
AI summary
In some examples, natural language processing based sign language generation may include ascertaining a speech video that is selected by a user, and determining, based on application of natural language processing to contents of the speech video, a plurality of sentences included in the speech video. For each sentence of the plurality of sentences identified in the speech video, a sign language sentence type, a sign language sentence structure, and a sentiment may be determined. For each sign language sentence structure and based on a corresponding sentiment, a sign video may be determined. Based on the sign video determined for each sentence of the plurality of sentences identified in the speech video, a combined sign video may be generated.