Two-Stage ML Transcription System with Fine-Tuning
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Solution Overview
Problem
Current automated transcription methods for written communications, such as handwritten documents, lack efficiency and reliability, particularly in adapting to user-specific data domains and varying script types like logographic, syllabic, and ideographic scripts.
Innovation Solution
A system that fine-tunes pre-trained machine learning models using user-specific data, allowing for the generation of refined transcriptions with improved confidence scores through a two-stage processing approach involving a first compute device for initial transcription and a second compute device for refinement, utilizing feature extraction and quality assurance programs to enhance automation performance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a single-stage transcription model is used, then the system complexity is low, but the transcription accuracy and reliability are insufficient
Solution Approach 1:
The patent divides the transcription system into two independent stages: a first transcription model that generates initial transcriptions and a second refinement model that improves accuracy. This segmentation allows each model to specialize in specific tasks, with the first model handling general transcription and the second model focusing on error correction and refinement, thereby achieving higher overall accuracy without requiring a single overly complex model
Solution Approach 2:
The first transcription model performs preliminary transcription work before the refinement stage. By pre-processing the document and generating an initial transcription, the system prepares data that the second refinement model can then optimize. This preliminary action reduces the computational burden on the refinement model and improves overall system efficiency
2Adaptability or versatility
If generic transcription models are used, then the system is easy to deploy, but the adaptability to user-specific data domains and script types is poor
Solution Approach 1:
The patent implements domain-specific adaptation by training the refinement model on user-specific data from particular domains (e.g., medical, legal, technical). This allows the model to develop specialized knowledge for specific script types and domains while maintaining the general transcription capability of the first model, achieving local optimization without sacrificing overall versatility
Solution Approach 2:
The system maintains universal applicability through the first transcription model that can handle various script types (logographic, syllabic, ideographic), while the second refinement model provides domain-specific enhancement. This multi-functional architecture allows the system to serve multiple domains and script types simultaneously
3Measurement precision
If manual review is used to improve transcription quality, then the transcription accuracy is high, but the productivity and automation performance are reduced
Solution Approach 1:
The second refinement model acts as an automated feedback mechanism that reviews and corrects transcriptions from the first model. By implementing this automated feedback loop, the system achieves high transcription quality without requiring manual human review for every document, thereby maintaining productivity and automation performance while improving accuracy
Solution Approach 2:
The refinement model performs self-correction of transcriptions without human intervention. The system automatically identifies and corrects errors in the initial transcription, enabling high-quality output while maintaining full automation and avoiding the need for manual proofreading
Data Source
AI summary
A non-transitory processor-readable medium stores instructions to be executed by a processor. The instructions cause the processor to receive a first trained machine learning model that generates a transcription based on a document. The instructions cause the processor to execute the first trained machine learning model and a second trained machine learning model to generate a refined transcription based on the transcription. The instructions cause the processor to execute a quality assurance program to generate a transcription score based on the document and the transcription. The instructions cause the processor to execute the quality assurance program to generate a refined transcription score based on the refined transcription and at least one of the document or the transcription. The at least one refined transcription score indicates an automation performance better than an automation performance for the at least one transcription score.