Speech Recognition Text Formatter with Adaptive Learning
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Solution Overview
Problem
Users of speech recognition systems for dictation often experience frustration due to repeated formatting errors, as current systems ignore user corrections and do not adapt formatting settings based on user feedback, requiring non-technical users to manually correct these errors.
Innovation Solution
A method and system that determine if a user correction can be addressed by changing a formatting setting, either through explicit user confirmation or inferential indication, allowing the system to automatically adjust settings based on user preferences without requiring users to navigate complex GUI options.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If users manually correct formatting errors through GUI settings, then formatting accuracy can be improved, but user complexity and operation difficulty increase significantly
Solution Approach 1:
The system automatically learns and adapts formatting preferences by monitoring user corrections, eliminating the need for users to manually configure formatting settings. The formatter self-adjusts based on observed user behavior patterns, making the system serve itself rather than requiring user intervention.
Solution Approach 2:
The system captures user corrections as feedback signals and uses them to automatically adjust formatting settings. By monitoring what users correct and how they correct it, the system learns optimal formatting preferences and applies them going forward, creating a closed-loop adaptive system.
2Adaptability or versatility
If formatting settings are manually configured through GUI, then formatting customization is achieved, but user time and operational effort are consumed
Solution Approach 1:
The system performs preliminary learning during initial use by monitoring user corrections, building a personalized formatting profile automatically. This preliminary action captures user preferences early, so that subsequent formatting operations are already customized without requiring dedicated setup time from the user.
Solution Approach 2:
The system autonomously configures formatting settings by observing and learning from user corrections, eliminating the need for users to spend time manually navigating GUI options. The formatter service configures itself based on observed user behavior patterns.
3Device complexity
If the system ignores user corrections, then system simplicity is maintained, but user frustration increases due to repeated errors
Solution Approach 1:
The system treats user corrections as valuable feedback rather than ignoring them. By capturing and analyzing correction patterns, the system learns from user interactions to improve formatting consistency, transforming what would be wasted corrections into learning opportunities that enhance reliability.
Solution Approach 2:
The system dynamically adjusts formatting parameters based on learned user preferences from corrections. By changing formatting parameters adaptively rather than statically, the system improves consistency and reduces repeated errors while maintaining reasonable system complexity through automated learning.
Data Source
AI summary
A computer implemented method and system of formatting text output from a speech recognition system is provided. The method includes determining if a user correction to a text output from a speech recognition system can be accomplished by changing a formatting setting associated with the speech recognition system. The formatting setting is changed based on an inferential indication that the change to the formatting setting is acceptable to the user and/or an explicit confirmation from the user that the change to the formatting setting is acceptable.