Alternative Input Representations for Speech Recognition Accuracy
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Solution Overview
Problem
Natural language processing systems, particularly those employing speech recognition and natural language understanding, often fail to accurately interpret user inputs due to errors in automatic speech recognition, leading to undesired actions or user frustration, especially when there is insufficient historic interaction data for alternative input representations.
Innovation Solution
A framework is introduced to test and collect feedback on alternative input representations by presenting them to users, storing user feedback, and updating the system to improve accuracy, while controlling the frequency and scope of testing to minimize user experience degradation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If alternative input representations are generated and tested frequently to improve accuracy, then user input interpretation accuracy is improved, but user experience degradation increases due to frequent testing interruptions
Solution Approach 1:
The system implements periodic testing of alternative input representations by triggering tests only under specific conditions (e.g., when ASR confidence is below a threshold or when historic interaction data indicates potential misinterpretation). This periodic approach allows accuracy improvement while minimizing continuous user experience degradation from frequent testing interruptions.
Solution Approach 2:
The system collects user feedback during testing and uses this feedback to refine future alternative input representation generation. By learning from user responses and ASR performance data, the system improves accuracy over time while reducing the need for continuous testing, thus balancing accuracy improvement with user experience.
2Measurement precision
If extensive historic interaction data is collected and processed to improve alternative input representations, then interpretation accuracy is improved, but system complexity increases
Solution Approach 1:
The system extracts and processes only the most relevant features from historic interaction data, such as ASR confidence scores, user feedback patterns, and contextual information. By focusing on key features rather than processing all available data, the system achieves improved accuracy while managing computational complexity.
Solution Approach 2:
The system performs preliminary processing of historic interaction data to pre-compute alternative input representations and store them for future use. This preliminary action reduces the computational burden during real-time processing while maintaining high accuracy through pre-processed data.
3Ease of operation
If alternative input representations are tested with limited frequency to maintain user experience, then user satisfaction is maintained, but interpretation accuracy improvement is slowed
Solution Approach 1:
The system uses periodic testing triggered by specific conditions such as low ASR confidence scores or when historic data indicates potential misinterpretation. This conditional periodic testing maintains user satisfaction by avoiding unnecessary interruptions while still achieving accuracy improvement through targeted testing.
Solution Approach 2:
The system automatically selects and tests alternative input representations based on pre-established criteria and historic data analysis, eliminating the need for manual intervention. This self-service approach enables continuous accuracy improvement without requiring user involvement in the testing process.
Data Source
AI summary
Techniques for presenting an alternative input representation to a user for testing and collecting processing data are described. A system may determine that a received spoken input triggers an alternative input representation for presenting. The system may output data corresponding to the alternative input representation in response to the received spoken input, and the system may receive user feedback from the user. The system may store the user feedback and processing data corresponding to processing of the alternative input representation, which may be later used to update an alternative input component configured to determine alternative input representations for spoken inputs.