Context-Specific Virtual Vocabulary for Speech Recognition Accuracy
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Solution Overview
Problem
Current speech recognition systems face challenges in accurately transcribing specific terms and context-specific inputs due to the use of general vocabularies that result in high error rates, particularly in form-based inputs where phonetically similar words cause misrecognition.
Innovation Solution
The method involves loading specified vocabularies associated with specific contexts or users into the system, evaluating user input against these vocabularies using evaluation criteria and matching heuristics, and selecting the most appropriate data values for form fields, with the option to default to a base vocabulary if the specified criteria are not met.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If general vocabularies are used for speech recognition, then the system can handle a wide range of inputs, but transcription accuracy deteriorates for specific context-related terms
Solution Approach 1:
The patent segments the vocabulary into multiple context-specific vocabularies (e.g., medical vocabulary, legal vocabulary) rather than using a single general vocabulary. Each vocabulary is tailored to specific domains or contexts, allowing the system to select the appropriate vocabulary based on the input context, thereby improving transcription accuracy for specific terms while maintaining overall versatility.
Solution Approach 2:
The system dynamically selects and switches between different vocabularies based on contextual analysis of the speech input. Rather than statically using one vocabulary, the system adapts its vocabulary selection in real-time based on detected context markers, form field types, and speech patterns, resolving the contradiction between broad coverage and specialized accuracy.
2Measurement precision
If context-specific vocabularies are used to improve accuracy, then transcription precision improves, but system complexity increases
Solution Approach 1:
The patent implements preliminary action by pre-loading and organizing multiple context-specific vocabularies into the system before operation. The system prepares vocabulary selection rules and context detection mechanisms in advance, so that during speech recognition, the appropriate vocabulary can be quickly selected without real-time complexity. This upfront preparation reduces operational complexity while maintaining high accuracy.
Solution Approach 2:
The system introduces an intermediary component (context analysis module) that mediates between the speech input and the vocabulary selection process. This intermediary analyzes contextual markers and determines which vocabulary to use, simplifying the overall system architecture by centralizing the decision-making logic and making the complexity manageable through modular design.
3Measurement precision
If multiple vocabularies are evaluated against user input, then transcription accuracy improves, but processing time increases
Solution Approach 1:
The patent applies local quality by evaluating different vocabularies with different scrutiny levels based on their relevance to the detected context. Highly relevant vocabularies (those matching the detected context) are evaluated with full detail, while less relevant vocabularies receive minimal or no evaluation. This selective evaluation approach maintains high accuracy for context-specific terms while reducing overall processing time.
Solution Approach 2:
The system performs partial evaluation of multiple vocabularies rather than exhaustive evaluation of all possible vocabularies. It uses context detection to identify the most relevant vocabulary subset and focuses computational resources on evaluating only those vocabularies, achieving high transcription accuracy without the time cost of evaluating every possible vocabulary option.
Data Source
AI summary
A virtual vocabulary database is provided for use with a with a particular user database as part of a speech recognition system. Vocabulary elements within the virtual database are imported from the user database and are tagged to include numerical data corresponding to the historical use of the vocabulary element within the user database. For each speech input, potential vocabulary element matches from the speech recognition system are provided to the virtual database software which creates virtual sub-vocabularies from the criteria according to predefined criteria templates. The software then applies vocabulary element weighting adjustments according to the virtual sub-vocabulary weightings and applies the adjustment to the default weighting provided by the speech recognition system. The modified weightings are returned with the associated vocabulary elements to the speech engine for selection of an appropriate match to the input speech.