Spoken Language Understanding Hierarchy for Error Control
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Solution Overview
Problem
Existing Spoken Language Understanding (SLU) systems fail to effectively handle vague utterances and do not utilize the intrinsic hierarchy of categories to minimize classification errors, leading to inadequate handling of calls that require machine intervention and increased user annoyance due to high-cost errors and low confidence recognition.
Innovation Solution
Organizing SLU categories into a hierarchical structure that accounts for varying levels of specificity, allowing for the trading of high-cost errors for low-cost errors by hypothesizing the parent category when recognition confidence is low, and implementing a confidence-based algorithm to manage errors by outputting a less specific category and requesting user confirmation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If SLU systems use well-defined categories with fixed specificity levels, then classification structure is simplified, but the system cannot effectively handle vague utterances and produces high-cost errors
Solution Approach 1:
The patent segments the category system into a hierarchical structure with multiple levels of specificity (e.g., L1, L2, L3 categories). Each level represents a different granularity of classification, allowing the system to match utterances at the appropriate level of detail. This segmentation enables handling of vague utterances by matching them to higher-level categories while maintaining detailed categories for specific cases.
Solution Approach 2:
The patent adds a hierarchical dimension to the traditional flat category structure. Instead of treating all categories as equal, the system organizes them in levels where L1 categories are less specific and L3 categories are more specific. This dimensional change allows the system to navigate between different levels of specificity based on utterance clarity.
2Device complexity
If SLU systems use flat category structures without hierarchy, then implementation is simpler, but errors between categories have equal impact and cannot be controlled
Solution Approach 1:
The patent segments errors into different types based on their impact: high-cost errors (between categories at the same level or in different hierarchical paths) and low-cost errors (between parent and child categories). This segmentation enables differential error handling where the system can accept certain errors (low-cost) while actively preventing others (high-cost).
3Measurement precision
If SLU systems recognize specific categories with high confidence, then classification precision is improved, but vague utterances result in high-cost errors
Solution Approach 1:
The patent implements preliminary action by checking confidence levels before finalizing category assignments. When confidence is low, the system proactively hypothesizes parent categories and requests user confirmation before proceeding. This preliminary verification prevents high-cost errors from occurring in the first place.
Solution Approach 2:
The patent implements feedback mechanisms where the system monitors confidence levels and adjusts its behavior accordingly. Low confidence triggers additional verification steps (hypothesis generation, user confirmation requests), while high confidence allows direct classification. This feedback loop ensures reliable classification across varying utterance qualities.
4Reliability
If SLU systems request user confirmation for low confidence recognition, then classification accuracy is improved, but interaction time increases
Solution Approach 1:
The patent applies local quality by requesting user confirmation only in specific situations (low confidence recognition) rather than for all classifications. The system locally adjusts its verification requirements based on the quality of each recognition event, minimizing unnecessary interaction delays while maintaining accuracy where needed.
Data Source
AI summary
A method for variable resolution and error control in spoken language understanding (SLU) allows arranging the categories of the SLU into a hierarchy of different levels of specificity. The pre-determined hierarchy is used to identify different types of errors such as high-cost errors and low-cost errors and trade, if necessary, high cost errors for low cost errors.