Voice Recognition Classifier Training via Automated NLP Evaluation
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Solution Overview
Problem
Current voice recognition assistance systems rely on subjective evaluations by technophile users, lacking objectivity in assessing the performance and effectiveness of classifiers, which hinders their ability to achieve service levels comparable to human professionals.
Innovation Solution
An automated method using natural language processing (NLP) algorithms to collect and process user data, generate classification outputs, and incorporate user feedback for evidence-based training of classifiers, enabling objective evaluation and improvement of natural language query classification.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If subjective evaluation by technophile users is used to assess classifier performance, then the evaluation process is simple to implement, but the objectivity and reliability of the assessment deteriorates
Solution Approach 1:
The patent introduces an intermediary automated evaluation system that acts as a mediator between the voice recognition system and human evaluators. This system uses NLP algorithms to objectively analyze classifier outputs, transforming subjective evaluation into an objective, automated process that maintains simplicity while improving reliability.
Solution Approach 2:
The patent replaces the mechanical system of human subjective evaluation with an automated NLP-based evaluation system. This substitution eliminates human bias and subjectivity while maintaining the ease of implementation through automated processing, directly resolving the contradiction between simplicity and objectivity.
2Reliability
If automated NLP-based evaluation is implemented to improve objectivity, then the reliability of classifier assessment is improved, but the device complexity and processing requirements increase
Solution Approach 1:
The patent applies multi-functionality by using the existing NLP algorithms already present in the voice recognition system for dual purposes: both for processing user queries and for evaluating classifier outputs. This universal approach improves reliability through objective automated evaluation while avoiding the need for separate complex evaluation infrastructure.
Solution Approach 2:
The evaluation system performs self-service by using the voice recognition system's own NLP capabilities to evaluate its classifier outputs. This self-evaluation approach improves objectivity without requiring external complex systems, as the system leverages its existing computational resources for both operation and self-assessment.
3Manufacturing precision
If comprehensive data collection and processing is performed to improve classification accuracy, then the manufacturing precision of classifier training is improved, but the loss of time and computational resources increases
Solution Approach 1:
The patent applies preliminary action by collecting and processing evaluation data continuously during system operation, before formal classifier retraining is needed. This ongoing data preparation ensures high-quality training data is ready when retraining occurs, improving classification accuracy while minimizing the time loss during actual retraining cycles.
Solution Approach 2:
The evaluation and data collection process operates continuously during system operation, maintaining a constant flow of labeled training data. This continuous action ensures that classifier training can be performed efficiently with up-to-date data, improving accuracy without requiring lengthy batch processing periods.
Data Source
AI summary
The disclosure relates to a method and system for training one or more classifiers for use in a voice recognition, VR, assistance system. The method comprises collecting data which contain one or more natural language queries to the VR assistance system; processing the data using a natural language processing, NLP, algorithm; generating a first classification output, based on the results of the NLP; obtaining a user input based on the first classification output; and generating a second classification output, based on the user input, for training the classifier.