Speech Recognition Mistranscription Analyzer Class Learning
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Solution Overview
Problem
Current speech recognition systems suffer from transcription errors such as substitution, insertion, and deletion errors, leading to incorrect command execution or transcription, particularly due to non-native speakers, sloppy speech, and background noise, necessitating manual correction or user interaction.
Innovation Solution
A method that organizes utterances into classes with a common meaning and response, using a mistranscription analyzer to increment evidence for mistranscriptions, adding new class members based on user feedback and confidence thresholds, allowing the system to treat recognized utterances with mistranscriptions as if the original word was recognized.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional speech recognition systems are used, then speech can be transcribed into written form, but transcription errors occur due to non-native speakers, sloppy speech, and background noise
Solution Approach 1:
The system implements feedback by monitoring transcription confidence scores and automatically initiating clarification dialogs when confidence falls below a threshold. The system learns from user corrections to improve future transcriptions, creating a closed-loop feedback mechanism that continuously reduces transcription errors.
Solution Approach 2:
The patent introduces an intermediary clarification dialog system that mediates between the speech recognition output and the final transcription. When uncertainty is detected, this intermediary layer engages the user for clarification before finalizing the transcription, thereby resolving the conflict between automated processing and accuracy.
2Reliability
If manual examination and correction of transcripts is performed, then transcription errors can be corrected, but user time and effort are consumed
Solution Approach 1:
The system performs self-service by automatically detecting and correcting its own transcription errors through confidence score monitoring and learned error patterns. The machine learning component continuously improves the system's ability to self-correct without requiring manual intervention, thereby maintaining high accuracy while minimizing user time investment.
Solution Approach 2:
User corrections are fed back into the system to train the machine learning model, creating a self-improving mechanism. Over time, the system learns from these feedback loops to automatically correct similar errors without requiring repeated manual intervention, reducing the time users need to spend on corrections.
3Reliability
If the speech recognition system asks users for clarification on candidate mistranscriptions, then accuracy can be improved, but system complexity and interaction overhead increase
Solution Approach 1:
The system applies partial action by selectively initiating clarification dialogs only when confidence scores fall below a threshold or when error patterns are detected, rather than universally for all transcriptions. This selective approach maintains accuracy for uncertain cases while avoiding unnecessary complexity for high-confidence transcriptions.
Solution Approach 2:
The system dynamically adjusts the confidence threshold parameter based on learned error patterns and context. This parameter change allows the system to adapt its clarification behavior, reducing complexity by raising thresholds for well-understood speech patterns while lowering them for problematic cases, thereby optimizing the balance between accuracy and interaction overhead.
4Reliability
If more speech samples are collected from a user, then the acoustic model can be better adapted, but data collection time and user burden increase
Solution Approach 1:
The system performs preliminary adaptation by using transfer learning from general speech data to quickly initialize user-specific models. This preliminary action reduces the amount of user-specific data needed, allowing the system to achieve good performance with minimal data collection time while still maintaining the ability to improve with additional samples.
Solution Approach 2:
The system enables continuous adaptation by incrementally learning from each user interaction rather than requiring batch data collection. The acoustic model continuously improves through online learning from ongoing speech samples, transforming data collection from a time-consuming preliminary task into an ongoing process that improves accuracy without significant user burden.
Data Source
AI summary
A mistranscription generated by a speech recognition system is identified. A first class of utterance members is provided for use by the speech recognition system, each utterance class member consisting of a respective number of words. The first class is defined by a first common meaning and a first common system response if a class member of the first class is recognized. If the speech recognition system matches a received utterance to a first class member of the first class, the received utterance is sent to a mistranscription analyzer. The received utterance contains a mistranscription as compared to the first class member. Evidence is incremented by the mistranscription analyzer that the received utterance is evidence of the mistranscription of the first class member. If the incremented evidence for the mistranscription of the first class member exceeds a first threshold, a second class member is added to the first class of utterance members based on the mistranscription of the first class member. If the speech recognition system recognizes a second received utterance which matches the second class member, performing the common system response.