Speech Recognition Uncertainty Isolation via Meta-Information Weighting
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Solution Overview
Problem
Existing speech recognition systems face challenges in accurately recognizing natural language phrases due to variability in speech quality, pronunciation, and phrasing, often requiring users to conform to constrained formats to mitigate uncertainty.
Innovation Solution
A modular system comprising an automated speech recognition engine, adaptive machine learning system, and natural-language processing engine that separates and optimizes each component's performance using meta-information, allowing for weighted adjustment of uncertainty metrics to improve recognition accuracy across different environments and applications.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If a single approach is used to compensate for both speech uncertainty and phrasing uncertainty, then the system can handle multiple types of uncertainty, but the recognition accuracy for natural language phrases deteriorates
Solution Approach 1:
The patent divides the uncertainty handling into separate modules: an ASR uncertainty module that processes speech recognition confidence metrics, and an NLP uncertainty module that handles phrasing and semantic ambiguity. This segmentation allows each module to specialize in handling specific types of uncertainty independently, improving overall recognition accuracy while maintaining versatility.
2Measurement precision
If the user is forced to use a highly constrained format for making requests, then the phrasing uncertainty is reduced, but the ease of operation deteriorates
Solution Approach 1:
The patent introduces an NLP module as an intermediary between the ASR and the final response generation. This intermediary layer processes the constrained ASR output and translates it into natural language responses, allowing users to speak freely while maintaining accurate phrasing recognition through the mediation of the NLP processing layer.
3Device complexity
If meta-information is shared between components with pre-determined weighting, then the system structure is simplified, but the adaptability to different problem domains and applications deteriorates
Solution Approach 1:
The patent implements dynamic weighting of meta-information through an adaptive learning system that adjusts the importance of ASR confidence metrics and NLP processing results based on the specific application domain and problem context. This dynamic adjustment mechanism allows the system to adapt to different domains without requiring complex pre-determined weighting rules for each scenario.
Data Source
AI summary
A speech recognition system includes a natural language processing component and an automated speech recognition component distinct from each other such that uncertainty in speech recognition is isolated from uncertainty in natural language understanding, wherein the natural language processing component and an automated speech recognition component communicate corresponding weighted meta-information representative of the uncertainty.