Speech Component Evaluation Using Synthetic User Inputs
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Solution Overview
Problem
Existing speech processing systems often produce undesired responses or errors due to misrecognition, misinterpretation, or misidentification of user inputs, which can be attributed to issues in automatic speech recognition (ASR), natural language understanding (NLU), and entity resolution (ER) processing.
Innovation Solution
A system and method for evaluating speech processing components using synthetic user inputs, allowing for offline evaluation of ASR, NLU, and ER tasks within a serverless computing framework, which includes generating and processing synthetic inputs to identify and reduce errors, and updating in-production components accordingly.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If speech processing systems use complex ASR, NLU, and ER processing to improve accuracy, then recognition and understanding quality improves, but system complexity and difficulty of detecting errors increases
Solution Approach 1:
The patent segments the speech processing system into distinct ASR, NLU, and ER processing stages, allowing independent evaluation of each component. This segmentation enables targeted testing and error identification without requiring evaluation of the entire complex system, thus managing complexity while maintaining high measurement precision.
Solution Approach 2:
The patent introduces synthetic user inputs as an intermediary mechanism for evaluation. These synthetic inputs serve as a mediator between the complex speech processing system and the evaluator, providing standardized test cases that can be systematically generated and processed to assess each component's performance independently.
2Productivity
If the system processes real user inputs to improve responsiveness, then user experience improves, but error detection and system improvement slows down
Solution Approach 1:
The patent applies preliminary action by generating and preparing synthetic user inputs in advance for systematic evaluation. These pre-generated test inputs allow the system to be evaluated thoroughly before deployment or during maintenance, enabling error detection and component improvement without delaying real-time user interactions.
Solution Approach 2:
The patent uses synthetic user inputs as copies or representations of real user inputs for evaluation purposes. These synthetic copies allow comprehensive testing of ASR, NLU, and ER processing without requiring actual user interactions, thus maintaining fast real-time response while enabling thorough error detection through repeated synthetic input processing.
3Reliability
If the system continuously updates components to fix errors, then system reliability improves, but operational complexity and risk of introducing new errors increases
Solution Approach 1:
The patent implements feedback mechanisms where synthetic input processing results are systematically analyzed to identify errors in ASR, NLU, or ER processing. This feedback loop provides structured information about system performance and specific component failures, enabling targeted updates that improve reliability while managing update complexity through systematic error analysis.
Solution Approach 2:
The patent replaces manual error detection and component updating with an automated evaluation system using synthetic inputs. This substitution of mechanical evaluation processes with systematic synthetic data processing and analysis enables more reliable and manageable updates, as errors are identified through consistent, repeatable testing rather than ad-hoc analysis.
Data Source
AI summary
Techniques for evaluating speech processing components are described. A system may receive a task request including at least a plurality of entity names to be evaluated by at least one speech processing component. The system may determine synthetic user inputs corresponding to the plurality of entity names, and may cause performance of a speech processing task using the synthetic user inputs. The speech processing task may be an ASR processing task, an NLU processing task, or an ER processing task. The system may compare the results of the speech processing task with ground truth data to determine if an error occurred during processing.