Speech Trait Feedback System for Subjective Criteria Evaluation
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Solution Overview
Problem
Current computing devices are unable to provide feedback on subjective criteria such as persuasiveness, engagement, or confidence in user speech, as they are limited to simplistic audio analysis and grammatical evaluations, failing to assess tonal attributes and aural qualities effectively.
Innovation Solution
The system evaluates multiple speaking traits like vocal fry, tag questions, uptalk, filler sounds, and hedge words by isolating audio constructs, scoring them based on predetermined thresholds, and weighting them according to empirical relationships with subjective criteria, allowing for computer-generated feedback on speech quality.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If simplistic audio comparison algorithms are used to evaluate user speech, then the analysis can be performed with simple technical means, but the system cannot provide feedback on subjective criteria such as persuasiveness, engagement, or confidence
Solution Approach 1:
The patent segments speech evaluation into multiple independent speaking trait detectors, each analyzing specific traits (vocal fry, tag questions, uptalk, filler sounds, hedge words). This segmentation allows the system to evaluate complex subjective criteria through multiple specialized components rather than requiring a single complex algorithm, thereby improving measurement precision while managing system complexity through modular design
Solution Approach 2:
The patent creates a universal feedback system that can evaluate multiple subjective criteria (persuasiveness, engagement, confidence) using the same multi-trait analysis framework. The speaking trait detectors and scoring system serve multiple evaluation purposes simultaneously, allowing the system to provide comprehensive feedback across different communication qualities without requiring separate specialized systems for each criterion
2Loss of information
If multiple speaking traits are detected and evaluated with weighting and amalgamation, then comprehensive feedback on subjective criteria can be provided, but the complexity of the analysis system increases significantly
Solution Approach 1:
The patent divides the comprehensive speech analysis into separate speaking trait detectors, each responsible for identifying specific traits (vocal fry, tag questions, uptalk, filler sounds, hedge words). This segmentation ensures that no speech quality information is lost by distributing analysis across specialized components, while the modular structure helps manage overall system complexity through clear division of labor
Solution Approach 2:
The patent merges the results from multiple independent speaking trait detectors through a scoring system that combines individual trait scores into an overall evaluation. This merging process integrates information from all detected traits while using weighting mechanisms to prioritize important traits, thereby maintaining information completeness while presenting a unified comprehensive feedback output
3Measurement precision
If empirical relationships and manual weighting are used to score speaking traits, then accurate feedback on subjective criteria can be generated, but the time and computational resources required increase
Solution Approach 1:
The patent employs predetermined weights and thresholds for speaking traits that are established in advance based on empirical relationships. This preliminary preparation of evaluation criteria allows the system to perform accurate measurements during actual speech analysis without requiring complex real-time calculations, thereby maintaining measurement precision while reducing processing time during feedback generation
Solution Approach 2:
The patent uses predefined parameter values (weights and thresholds) that can be adjusted based on different evaluation contexts. By having these parameters预先 determined and stored, the system can quickly apply appropriate measurement standards without recalculating them during processing, balancing accuracy requirements with time efficiency through parameter optimization
Data Source
AI summary
Computer-generated feedback directed to whether user speech input meets subjective criteria is provided through the evaluation of multiple speaking traits. Initially, discrete instances of various multiple speaking traits are detected within the user speech input provided. Such multiple speaking traits include vocal fry, tag questions, uptalk, filler sounds and hedge words. Audio constructs indicative of individual instances of speaking traits are isolated and identified from appropriate samples. Speaking trait detectors then utilize such audio constructs to identify individual instances of speaking traits within the spoken input. The resulting quantities are scored based on reference to predetermined threshold quantities. The individual speaking trait scores are then amalgamated utilizing a weighting that is derived based on empirical relationships between those speaking traits and the criteria for which the user's speech input is being evaluated. Further adjustments thereof can be made by separately, manually weighting the previously determined quantities.