Transliteration Work Support Device for Voice Synthesis Error Correction
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current technologies face challenges in efficiently supporting transliteration work for documents, particularly in accurately converting Kanji and Kana characters into natural-sounding voice synthesis, especially in handling accent and reading errors, which hinders effective voice synthesis for individuals with visual or learning disabilities.
Innovation Solution
A transliteration work support device comprising an analysis unit for language analysis, an estimation unit for identifying correction places and candidates, a construction unit for creating work lists, and a correction unit for updating data, which assists operators in efficiently correcting and improving voice synthesis quality by prioritizing specific work items based on statistical information and correction history.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If voice synthesis technology is used to replace real voices, then accessibility for individuals with visual or learning disabilities is improved, but accuracy in handling accent and reading errors deteriorates
Solution Approach 1:
The system performs preliminary language analysis on document data before voice synthesis, identifying potential accent and reading errors in advance. The analysis unit extracts linguistic features and compares them against stored correction histories to pre-correct errors before the text is converted to speech, ensuring accurate voice synthesis while maintaining accessibility.
Solution Approach 2:
The system incorporates feedback mechanisms where correction results from previous transliteration work are stored and reused. The correction history storage unit preserves learned corrections, and the system continuously improves by applying feedback from past corrections to future voice synthesis tasks, progressively enhancing accuracy.
2Measurement precision
If manual transliteration work is performed to ensure accuracy, then voice synthesis quality is improved, but work efficiency deteriorates
Solution Approach 1:
The system performs self-correction by automatically analyzing document data, identifying potential errors, and applying corrections based on stored correction histories. The language analysis unit and correction application mechanism enable the system to self-improve voice synthesis quality without requiring continuous manual intervention, significantly enhancing transliteration work efficiency.
Solution Approach 2:
Correction histories from previous manual transliteration work are stored and reused for future documents. The system performs preliminary correction based on historical data before manual review, reducing the amount of manual work needed while maintaining high voice synthesis quality.
3Measurement precision
If comprehensive language analysis is performed to identify all correction places, then accuracy is improved, but processing time deteriorates
Solution Approach 1:
The language analysis unit focuses on analyzing specific linguistic features and local patterns that are most likely to contain accent and reading errors, rather than performing exhaustive analysis on the entire document. This targeted approach identifies correction places with high accuracy while minimizing processing time.
Solution Approach 2:
The system applies partial correction by focusing on the most critical error types (accent and reading errors) identified through language analysis, rather than attempting to correct all possible issues. This selective correction approach maintains high accuracy for the most important errors while reducing overall processing time.
Data Source
AI summary
According to an embodiment, a transliteration work support device includes an analysis unit, a storage unit, an estimation unit, a construction unit, a correction unit, and an update unit. The analysis unit performs language analysis on document data and creates transliteration auxiliary information representing a way of transliteration of a word or a phrase in the document data. The storage unit stores a correction history representing a way of transliteration corrected in the past of the word or the phrase. The estimation unit estimates a correction place and a correction candidate of the document data or the transliteration auxiliary information from the history. The construction unit constructs work list information including work items corresponding to types of corrections according to the correction candidate and progress information. The correction unit corrects the document data or the transliteration auxiliary information. The update unit updates the history and the progress information according to the correction.