Speech Analytics Confidence Threshold Optimization
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Solution Overview
Problem
Speech analytics systems in contact centers face challenges in accurately detecting targeted phrases in real-time, leading to false positives and false negatives, which are time-consuming and resource-intensive to optimize due to the need for manual verification and the complexity of parameter settings.
Innovation Solution
A method to determine an optimized confidence score threshold level by processing audio instances with an initial threshold, manual verification, and subsequent reprocessing with different threshold values to minimize errors, allowing for automated optimization and improved accuracy in detecting target words.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual verification is used to optimize speech analytics parameters, then accuracy of target word detection is improved, but time and resources required for optimization increase
Solution Approach 1:
The system performs preliminary processing by automatically generating multiple parameter settings and their corresponding performance metrics before manual verification is needed. This preliminary automation reduces the scope of manual work to only verifying and selecting from pre-computed options, rather than manually testing each parameter combination.
Solution Approach 2:
The speech analytics system performs self-optimization by automatically evaluating different parameter settings against stored audio data and generating performance metrics. The system serves itself by identifying promising parameter combinations that require minimal manual verification, reducing dependency on continuous manual intervention.
2Reliability
If multiple parameter values are tested to improve detection accuracy, then false positives and false negatives are reduced, but system complexity increases
Solution Approach 1:
The system segments the parameter optimization process into distinct automated stages: generating parameter variations, processing audio data with each setting, and evaluating performance metrics. This segmentation allows complex multi-parameter testing to be broken down into manageable, automated steps that reduce overall system complexity.
Solution Approach 2:
The system automatically generates and tests multiple parameter values by systematically varying confidence score thresholds and other detection parameters. This automated parameter exploration allows the system to find optimal settings without requiring complex manual configuration, as the automation handles the complexity of testing multiple parameter combinations.
3Productivity
If real-time processing is implemented, then operational efficiency is improved, but accuracy of phrase detection may be reduced
Solution Approach 1:
The system performs preliminary optimization offline by processing stored audio data with multiple parameter settings before deployment. This preliminary action allows the system to identify optimal parameters without requiring real-time computation, enabling accurate detection while maintaining real-time processing capabilities during actual operation.
Solution Approach 2:
The system creates copies of audio data for offline processing and parameter optimization. By working with copied stored audio rather than requiring real-time audio streams during the optimization phase, the system can perform comprehensive accuracy testing without impacting real-time operational efficiency.
Data Source
AI summary
An approach for optimizing a confidence score threshold that is used to recognize a target word(s) in an audio source. A variety of potential instances of the target word can be detected and classified using an initial confidence score threshold value. Each potential instance of the target word is audibly reviewed and validated by a user. After a determination of the correctness of each potential instance's classification, a different confidence score threshold value can be used to produce an updated set of classification results without requiring the user to revalidate the results. By using a variety of confidence score threshold values to produce various sets of classification results, an optimized confidence threshold setting can be determined for the identified target word based on minimizing errors in the various results. This value can then be applied for future analysis of the target word in an audio source.