Labeling Support Device for Contact Center Voice Recognition
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Solution Overview
Problem
Existing techniques for improving service quality in contact centers through voice recognition require a large amount of training data and manual label assignment, which is time-consuming and burdensome for workers, especially when labels have a hierarchical structure without guidelines.
Innovation Solution
A label assignment support device and method that uses an existing model to estimate preliminary labels, generates a label assignment work screen for updating labels, and arranges elements and labels in a structured format to facilitate efficient label assignment by workers, reducing the need for extensive manual updating.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual label assignment is performed for creating training data, then labeling accuracy can be ensured, but the time and effort required for label assignment increases significantly
Solution Approach 1:
The system performs preliminary label assignment using an existing model before manual review. This preliminary labeling step provides a starting point that is already reasonably accurate, reducing the amount of manual work needed while maintaining high final labeling accuracy.
Solution Approach 2:
The existing model acts as an intermediary between complete manual labeling and no labeling. It provides preliminary labels that serve as a foundation, which workers then review and correct, rather than creating labels from scratch or relying solely on automated labels.
2Adaptability or versatility
If workers assign labels without guidelines for hierarchical label structures, then flexibility is maintained, but the burden and complexity of label assignment increases
Solution Approach 1:
The system displays the hierarchical label structure and relationships to workers during the label assignment process. This feedback mechanism helps workers understand how labels connect and constrain each other, making the process more intuitive and easier to operate while maintaining flexibility.
Solution Approach 2:
The label assignment process is segmented into different levels of the hierarchy. Workers can work with high-level labels first, then progressively refine to more specific subordinate labels, breaking down the complex task into manageable segments that are easier to handle.
3Measurement precision
If a large amount of training data is collected to improve model accuracy, then estimation accuracy improves, but the work required to create and process the training data increases
Solution Approach 1:
The system performs preliminary label assignment using an existing model before manual review. This preliminary labeling step provides a starting point that is already reasonably accurate, reducing the amount of manual work needed while maintaining high final labeling accuracy.
Solution Approach 2:
The existing model serves itself by generating preliminary labels that can be reviewed and corrected by workers. This self-service capability reduces the overall workload compared to complete manual labeling, improving productivity while still achieving high accuracy through the combination of automated preliminary work and human review.
Data Source
AI summary
A label assignment support device according to the present disclosure includes a preliminary label estimation unit that assigns preliminary labels for each of a plurality of elements, a label assignment work screen output unit that generates a label assignment work screen for each of the plurality of elements and an update operation for labels assigned to the plurality of elements by a user, the label assignment work screen indicating each of the plurality of elements and labels assigned to each of the plurality of elements in association with each other, and a label update unit that, when a label assigned to one of the elements is updated by the update operation via the label assignment work screen, assigns the label after update to the one of the elements.