Real-Time Conversational Analytics for Meeting Emotion Detection
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Solution Overview
Problem
Current analytics methods primarily focus on recorded data and lack the ability to effectively analyze real-time vocal and video recordings for emotional and role determination in conversational settings, such as meetings, which limits their applicability in dynamic communication scenarios.
Innovation Solution
A computer-implemented method and system that analyzes vocal and video recordings using predefined parameters for speech and gestures to determine emotions and roles in conversations, providing real-time feedback and suggestions to users through a conversational analytics program.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If analytics are performed on recorded data using traditional methods, then data patterns can be discovered, but real-time emotional and role determination in conversational settings cannot be achieved
Solution Approach 1:
The system transitions from static analysis of recorded data to dynamic real-time analysis of vocal and video recordings during conversations. The analytics engine continuously processes incoming audio and video streams to determine emotions and roles as they occur, enabling adaptability to changing conversational contexts while maintaining measurement precision through multi-parameter analysis
Solution Approach 2:
The analytics system is designed to handle multiple types of data (vocal recordings, video recordings) and perform multiple functions (emotion determination, role determination, conversation analysis) within a single unified platform. This multi-functional approach enables the system to address various analytical needs in conversational settings simultaneously
2Productivity
If traditional analytics methods are used on recorded data, then statistical patterns can be identified, but real-time feedback for communication enhancement cannot be provided
Solution Approach 1:
The system performs preliminary analysis of vocal and video parameters in real-time as data is being captured, rather than waiting for complete recording sessions. By continuously processing incoming streams and providing incremental analytics feedback, the system enables immediate communication adjustments without requiring post-processing time delays
Solution Approach 2:
The analytics engine implements a feedback mechanism that provides real-time insights about emotions, roles, and conversation dynamics back to the users during the interaction. This continuous feedback loop enables participants to adjust their communication strategies immediately based on analytical insights, thereby enhancing communication efficiency without time loss
Data Source
AI summary
A computer-implemented method includes determining a meeting has initialized between a first user and a second user, wherein vocal and video recordings are produced for at least the first user. The method receives the vocal and video recordings for the first user. The method analyzes the vocal and video recordings for the first user according to one or more parameters for speech and one or more parameters for gestures. The method determines one or more emotions and a role in the meeting for the first user based at least on the analyzed vocal and video recordings. The method sends an output of analysis to at least one of the first user and the second user, wherein the output of analysis includes at least the determined one or more emotions and the role in the meeting for the first user.