Tracker Model Generation for Conversation Analysis
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Solution Overview
Problem
Current methods for processing conversational speech face challenges in capturing insights due to variations in speech patterns, mid-conversation context switches, domain-specific vocabulary, and background noise, making it costly or impossible to train effective machine learning models for analyzing conversations effectively.
Innovation Solution
A system that utilizes a universal generalization model to predict a generalized vocabulary from a tracker vocabulary, generating a tracker model that scores matches in a conversation stream, allowing for real-time tracking of concepts, topics, or activities, and collecting metrics for reporting and notifications.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If machine learning models are trained to analyze conversations with variations in speech patterns, context switches, domain-specific vocabulary, and background noise, then analysis accuracy is improved, but training cost and complexity become prohibitively expensive or impossible
Solution Approach 1:
The system segments the conversation analysis task into multiple independent tracker models, each specialized for detecting specific vocabulary items or concepts. Instead of training one comprehensive complex model, the system creates multiple simple specialized models that can be trained independently and then combined, reducing the complexity and cost of training while maintaining high accuracy.
Solution Approach 2:
The system employs a universal tracker model architecture that can be adapted to detect any vocabulary item or concept by simply providing different training data. This universal model serves multiple functions across different domains and conversation types, eliminating the need to train separate specialized models for each domain or vocabulary set, thereby reducing overall training complexity.
2Measurement precision
If comprehensive machine learning models are trained to handle all speech variations and contexts, then tracking precision is improved, but resource consumption and training time increase significantly
Solution Approach 1:
The training process is segmented into multiple small, focused training tasks for each tracker model rather than one large comprehensive training task. Each tracker model is trained quickly on its specific vocabulary, and the overall system achieves comprehensive coverage through the combination of multiple quickly-trained models, significantly reducing total training time.
Solution Approach 2:
The system uses a standardized tracker model template that can be rapidly copied and instantiated for different vocabulary items. Instead of training unique complex models for each keyword or concept, the system copies a proven working model architecture and retrains it with specific vocabulary data, dramatically reducing training time while maintaining precision.
3Device complexity
If simple keyword tracking is implemented, then system complexity is reduced, but ability to capture contextually similar phrases and emerging trends is insufficient
Solution Approach 1:
The tracker models use adjustable parameters such as confidence thresholds, match criteria, and vocabulary sets that can be modified to detect contextually similar phrases rather than just exact keyword matches. This allows the simple tracker architecture to become versatile in recognizing variations and emerging trends while maintaining low system complexity.
Solution Approach 2:
The system incorporates feedback mechanisms where tracker results are analyzed to identify emerging trends and contextually similar phrases. The feedback loop allows the system to adapt and improve its tracking capabilities over time without increasing the fundamental complexity of the tracker models, enabling versatile phrase recognition through iterative refinement.
Data Source
AI summary
Embodiments are directed to organizing conversation information. A tracker vocabulary may be provided to a universal model to predict a generalized vocabulary associated with the tracker vocabulary. A tracker model may be generated based on the portions of the universal model activated by the tracker vocabulary such that a remainder of the universal model may be excluded from the tracker model. Portions of a conversation stream may be provided to the tracker model. A match score may be generated based on the track model and the portions of the conversation stream such that the match score predicts if the portions of the conversation stream may be in the generalized vocabulary predicted for the tracker vocabulary. Tracker metrics may be collected based on the portions of the conversation and the match scores such that the tracker metrics may be included in reports or notifications.