Error Attribution in Speech Processing Systems
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Solution Overview
Problem
Speech processing systems often fail to accurately interpret user inputs due to errors in automatic speech recognition (ASR) and natural language understanding (NLU), leading to undesired responses or system failures, making it difficult to identify the root cause of these errors.
Innovation Solution
A system that classifies user inputs into specific error types, such as ASR errors, intent errors, or entity errors, using a trained model and feature vectors to determine the root cause of processing errors, allowing for targeted retraining and improvement of system components.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If speech processing systems perform ASR and NLU processing to interpret user inputs, then the system can respond to user commands, but errors in processing lead to undesired responses or system failures
Solution Approach 1:
The patent segments the speech processing system into distinct components (ASR, NLU, dialog management) and introduces separate error attribution mechanisms for each component. This segmentation allows independent error detection and diagnosis without disrupting the overall user interaction capability.
Solution Approach 2:
The patent introduces an intermediary error attribution system that mediates between the speech processing components and the output generation. This intermediary analyzes processing outputs, identifies errors, and provides corrective feedback without directly interfering with the core ASR and NLU operations.
2Adaptability or versatility
If the system processes user inputs through multiple components (ASR, NLU), then comprehensive interpretation is achieved, but it becomes difficult to identify the root cause of errors
Solution Approach 1:
The patent divides the error detection task into segment-specific analyses, with dedicated error attribution logic for ASR errors, NLU errors, and dialog management errors. Each segment has its own error indicators and diagnostic criteria, making root cause identification straightforward despite the multi-component architecture.
Solution Approach 2:
The patent implements feedback mechanisms where each processing component outputs error indicators that are fed back to the error attribution system. This feedback loop enables automatic identification of which component generated the error, simplifying diagnosis while maintaining comprehensive interpretation capabilities.
3Ease of operation
If speech processing systems are designed to handle various user inputs, then user experience is enhanced, but the frequency of undesired actions increases due to processing errors
Solution Approach 1:
The patent implements error checking and attribution mechanisms that operate in advance of error propagation. By detecting and flagging errors early in the processing pipeline, the system prevents undesired responses before they occur, cushioning against harmful outcomes while maintaining user experience.
Solution Approach 2:
The patent uses feedback from error attribution results to adjust processing parameters and prevent recurring errors. When errors are detected, the system learns from them and modifies its behavior to reduce the frequency of similar errors, thereby decreasing undesired responses while preserving user experience enhancements.
Data Source
AI summary
A system is provided for determining which component of a speech processing system is the cause of an undesired response to a user input. The system processes ASR data and NLU data to determine the component likely to cause the undesired response. Based on which component is the cause of the undesired response, the system performs an appropriate conversation recovery technique to confirm the speech processing results with the user.