Voice Emotion Concordance Detection From Speech Reports
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Solution Overview
Problem
Existing methods for self-reported emotion detection in speech lack accuracy, as individuals may not accurately capture their true emotional state, leading to discrepancies between perceived and expressed emotions, which hinders mental health management and wellness applications.
Innovation Solution
A system that combines self-reported emotions with automatically detected emotions using voice analysis, employing multi-label classification neural networks and Generative AI Large Language Models to identify discrepancies and provide concordance-discrepancy reports.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If self-reported emotion detection is used, then ease of operation is improved, but measurement precision deteriorates
Solution Approach 1:
The patent combines self-reported emotion detection with automatically detected emotions from voice analysis into a unified system. The concordance-discrepancy report merges both data sources to provide a more accurate overall emotion assessment, resolving the contradiction by integrating subjective self-reporting (easy to operate) with objective acoustic analysis (high measurement precision).
Solution Approach 2:
The system provides feedback to users by displaying concordance-discrepancy reports that show both their self-reported emotions and the emotions detected by the system. This feedback loop allows users to compare their perceptions with objective measurements, improving measurement precision while maintaining ease of operation through simple voice input and visual feedback.
2Measurement precision
If automatic emotion detection is used, then measurement precision is improved, but device complexity increases
Solution Approach 1:
The patent introduces an intermediary processing layer that analyzes acoustic features (pitch, intensity, spectral characteristics) to detect emotions automatically. This intermediary system bridges the gap between simple voice input and complex emotion detection, improving measurement precision while managing device complexity through modular architecture that separates feature extraction, emotion classification, and report generation.
3Measurement precision
If concordance-discrepancy analysis is implemented, then measurement precision is improved, but loss of information increases
Solution Approach 1:
The system segments the emotion detection process into distinct components: self-reported emotions, automatically detected emotions, concordance analysis, and discrepancy analysis. Each segment processes specific information independently, then integrates results to improve measurement precision while preserving all original information through structured storage and presentation in the concordance-discrepancy report.
Data Source
AI summary
The present disclosure relates to the recognition of emotion in speech, both what is said and how it is said and the detection of a possible concordance or discrepancy between the two. A method is described for generating a time-stamped history of emotions a user reports by voice along with emotions detected automatically from those voice reports. A user utterance is analyzed using speech-to-text processing and a natural-language processing model to determine the emotion the user reports feeling. The user utterance is also analyzed using acoustic analysis to detect the emotion expressed in the user's voice report. A harmony report is generated from the time-stamped reports of concordance and discrepancy to measure the extent to which the user's perception of their emotions agrees with the emotions detected. The purpose of the invention is to provide insight into a user's emotions in real time and over time.