ASR Error Detection via Alternative Result Comparison

The patent introduces an evaluation engine as an intermediary component between the ASR system and the final output. This evaluation engine receives the recognition result and alternative results from the ASR system, compares them using domain-specific criteria (particularly for medical domains), and determines whether the result contains potential significant errors before passing it to the user interface. This intermediary layer resolves the contradiction by maintaining high conversion efficiency while improving reliability through automated error detection.

The patent implements a self-service mechanism where the system automatically evaluates its own recognition results without requiring manual human review. The evaluation engine uses domain-specific knowledge (particularly medical terminology and confusable word pairs) to autonomously identify potential significant errors by comparing the recognition result with alternative results. This self-service approach resolves the contradiction by achieving high error detection accuracy through automated domain-specific analysis while eliminating time loss associated with manual review processes.

The patent segments the error detection function into a separate, dedicated evaluation engine that operates independently from the core ASR recognition process. This segmentation allows the system to maintain simple, efficient speech-to-text conversion while adding a modular error detection component that compares recognition results with alternative results using domain-specific criteria. The segmented architecture resolves the contradiction by improving error identification reliability through specialized comparison logic without significantly increasing overall system complexity, as the evaluation engine operates as an independent module.