Random Confirmation Mechanism for Speech Recognition Accuracy
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Solution Overview
Problem
Conventional speech systems with deterministic confirmation mechanisms struggle to assess the performance of speech recognition results within certain confidence score ranges, leading to potential errors in accepting or rejecting recognized inputs without adequate verification.
Innovation Solution
A configurable confirmation mechanism that allows for random confirmation decisions based on confidence scores, enabling automated or human-operated verification of speech recognition results, and integrating deterministic and random confirmation modes for improved assessment.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If deterministic confirmation mechanism is used, then system operation is simple and straightforward, but the ability to assess speech recognition performance is insufficient leading to potential errors
Solution Approach 1:
The confirmation mechanism transitions from a static deterministic approach to a dynamic probabilistic approach. The system randomly selects speech recognition results for confirmation based on a predetermined probability, allowing the confirmation behavior to vary dynamically rather than following fixed deterministic rules. This resolves the contradiction by introducing randomness that improves reliability assessment without requiring complex deterministic decision logic.
Solution Approach 2:
The system changes the confirmation parameter from a deterministic binary decision (confirm or not confirm based on fixed thresholds) to a probabilistic parameter (random selection with predetermined probability). This parameter change enables the system to assess speech recognition performance more reliably by sampling a subset of results for confirmation, without increasing the complexity of the confirmation mechanism itself.
2Reliability
If confirmation is performed on all speech recognition results, then accuracy assessment is comprehensive, but system efficiency and response time deteriorate
Solution Approach 1:
Instead of performing confirmation on all speech recognition results (excessive action), the system performs confirmation on a randomly selected subset of results (partial action). The predetermined probability determines the portion of results that undergo confirmation, providing sufficient accuracy assessment without the overhead of confirming every single result. This resolves the contradiction between comprehensive accuracy assessment and system efficiency.
Solution Approach 2:
The system uses its own speech recognition results to self-assess its performance through random confirmation. By selecting a subset of its own outputs for verification, the system can monitor and evaluate its accuracy without requiring external validation for every result, thereby maintaining efficiency while improving reliability assessment.
3Loss of time
If no confirmation is performed, then system response time is fast, but error detection capability is lost
Solution Approach 1:
The system performs confirmation partially rather than completely - only on a randomly selected subset of speech recognition results determined by a predetermined probability. This partial confirmation approach detects errors in the sampled results without requiring time-consuming confirmation of every single result, thus balancing response time with error detection capability.
Data Source
AI summary
Method and system are provided for performing random confirmation in a speech system. When a speech recognition result is received with an associated confidence score indicating a level of confidence with respect to the speech recognition result, a confirmation decision is made in terms of whether a confirmation is to be carried out based on the confidence score. The confirmation decision may be made in a random confirmation mode. A confirmation may be performed when the confirmation decision is to carry out a confirmation on the speech recognition result.