Voice Translation System with Confidence-Based User Confirmation
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Solution Overview
Problem
Current on-line voice translation systems experience errors in voice recognition and translation, leading to non-smooth cross-language communication due to inaccuracies in recognizing and translating spoken language.
Innovation Solution
The method involves conducting voice recognition on user input, prompting the user to confirm recognition results based on confidence levels, translating confirmed information, extracting associated information from feedback, and correcting translations to improve accuracy. This includes determining confidence levels using key words and language rules within the dialogue context, and using key word similarity matching to rectify translation errors.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If voice recognition is conducted automatically without user confirmation, then processing speed is improved, but recognition accuracy deteriorates due to errors in recognizing spoken language
Solution Approach 1:
The system implements feedback by presenting recognized text to users for confirmation and allowing them to correct recognition errors. The translation system uses this feedback to improve future recognition accuracy, resolving the contradiction between fast automatic processing and accurate recognition.
Solution Approach 2:
The system performs preliminary voice recognition automatically to generate candidate text, then prepares it for user confirmation before final translation. This preliminary action enables fast initial processing while allowing subsequent accuracy improvement through user feedback.
2Speed
If translation is performed directly without correction mechanisms, then translation speed is improved, but translation quality deteriorates due to errors in cross-language translation
Solution Approach 1:
The system implements feedback loops where translation results are presented to users for confirmation and correction. These corrections are fed back to improve future translation quality, enabling the system to maintain both speed and quality through iterative improvement.
Solution Approach 2:
The translation system performs self-correction by learning from user feedback on translation errors. The system automatically improves its translation quality based on accumulated correction data without requiring manual intervention for each translation.
3Measurement precision
If user confirmation is required for all recognition results, then recognition accuracy is improved, but communication efficiency deteriorates due to additional interaction steps
Solution Approach 1:
The system applies user confirmation selectively rather than universally. It presents only uncertain recognition results or key information requiring verification to user confirmation, while automatically processing high-confidence translations. This partial application of confirmation maintains accuracy for critical elements while preserving overall communication efficiency.
Data Source
AI summary
Disclosed are on-line voice translation method and device. The method comprises: conducting voice recognition on first voice information input by a first user, so as to obtain first recognition information; prompting the first user to confirm the first recognition information; translating the confirmed first recognition information to obtain and output first translation information; extracting, according to second information which is fed back by a second user, associated information corresponding to the second information; and correcting the first translation information according to the associated information and outputting the corrected translation information. By means of the on-line voice translation method and device, smooth communication can be ensured in cross-language exchanges.