Voice-Based Emotion Tagging for Real-Time Content Ranking
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Solution Overview
Problem
Existing emotion tagging systems fail to accurately assign emotion tags to content based on the user's current emotional state during the viewing of content, as they rely on past emotional information from facial expressions rather than real-time user emotions.
Innovation Solution
An emotion tag assigning system that utilizes a voice detector to capture real-time voice data during an event, an emotion recognizer to analyze user emotions, and a processor to assign an emotion rank as a tag to the content, utilizing machine learning for accurate emotion recognition.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If emotion information is estimated from facial expressions of persons in captured images, then emotion tags can be assigned to content data, but the emotion information reflects past emotions at the time of capture rather than current emotions during viewing
Solution Approach 1:
The patent replaces the optical-based facial expression analysis system with an acoustic-based voice analysis system. Instead of analyzing facial expressions from captured images, the system analyzes voice data (acoustic signals) to recognize emotions, thereby obtaining real-time emotion information during content viewing rather than past emotions from capture time
Solution Approach 2:
The patent introduces voice data as an intermediary medium to bridge the gap between content viewing and emotion recognition. Voice data serves as a real-time indicator of user emotions during viewing, mediating the connection between the content being viewed and the user's current emotional state, thereby solving the timing mismatch problem
2Measurement precision
If fixed-point observation cameras are used to capture viewing environment images, then person recognition and emotion estimation can be performed, but the system complexity increases and real-time emotion tracking becomes difficult
Solution Approach 1:
The patent extracts the emotion recognition function from the complex fixed-point observation camera system. Instead of using multiple cameras and complex image processing, the system extracts only the essential voice data from the acoustic environment, simplifying the overall system while maintaining emotion recognition capability
Solution Approach 2:
The patent replaces expensive and complex fixed-point observation cameras with simple acoustic sensors (microphones) that can capture voice data. The acoustic field acts as a transient but sufficient medium for emotion detection, eliminating the need for complex visual monitoring systems
3Productivity
If tag information is updated based on emotion changes from facial expressions, then content retrieval can be optimized, but the system cannot accurately reflect real-time user emotions during event execution
Solution Approach 1:
The patent introduces real-time feedback by continuously analyzing voice data during content viewing and updating emotion tags accordingly. The system monitors voice emotions during event execution and dynamically adjusts content selection and presentation based on real-time user emotional responses, ensuring high reliability of emotion tags during actual events
Data Source
AI summary
Provided are an emotion tag assigning system, method, and program for assigning, to a content, an emotion tag indicating an emotion of a user in execution of an event using the content.An emotion tag assigning method includes a step of detecting, by a voice detector, voice data indicating a voice uttered by a person who participates in an event using a content during execution of the event; a step of recognizing, by an emotion recognizer, an emotion of the person based on the voice data; a step of acquiring, by a processor, emotion information indicating the recognized emotion of the person during the execution of the event using the content; and a step of assigning, by the emotion recognizer, an emotion rank calculated from the acquired emotion information to the content as an emotion tag.