Conference Speech Pattern Detection for Interruption-Aware Features
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing videoconferencing systems fail to analyze speech patterns to distinguish between a user pausing during speech and terminating speech, leading to frequent interruptions and frustration among participants, and lack features that adapt to interruption events.
Innovation Solution
Implementing a system that analyzes speech patterns to identify unique characteristics of each participant, determining speech events such as pauses or terminations, and activates or deactivates conferencing features like mute or highlight based on these patterns to manage interruptions intelligently.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If user-activated mute features are provided without speech pattern analysis, then system simplicity is maintained, but interruption management effectiveness deteriorates
Solution Approach 1:
The system automatically analyzes speech patterns and activates/deactivates conferencing features without requiring user intervention. The conference management system performs speech event detection, pause detection, and interruption detection autonomously, allowing the system to serve itself in managing conference dynamics rather than relying on manual user control.
Solution Approach 2:
The patent replaces manual user activation of mute features with automated speech pattern analysis. Instead of relying on mechanical user actions (clicking mute buttons), the system uses acoustic signal processing and machine learning models to detect speech events, pauses, and interruptions, substituting the mechanical control system with an intelligent automated system.
2Measurement precision
If speech pattern analysis is implemented to distinguish pauses from terminations, then speech event detection accuracy is improved, but processing complexity increases
Solution Approach 1:
The speech analysis process is divided into distinct segmentation steps: speech event detection identifies when speech occurs, pause detection analyzes gaps in speech to determine if the speaker is temporarily stopping or finished, and interruption detection identifies when multiple speakers talk simultaneously. Each segmentation handles a specific aspect of speech analysis, making the overall complex process more manageable and accurate.
Solution Approach 2:
The system performs preliminary speech pattern analysis by detecting speech events and analyzing pause patterns before making decisions about conference feature activation. The machine learning models are trained in advance on speech data to recognize patterns of pauses versus terminations, allowing the system to make accurate real-time decisions without complex on-the-fly processing.
3Ease of operation
If conferencing features are automatically activated based on speech patterns, then interruption management is enhanced, but loss of user control increases
Solution Approach 1:
The system provides feedback by monitoring speech patterns and automatically adjusting conference features based on detected speech events. When interruptions are detected, the system can automatically activate mute features for interrupting participants. This feedback loop allows the system to respond dynamically to conference dynamics while maintaining user-defined preferences and boundaries through the interruption threshold configuration.
Data Source
AI summary
Methods, systems, and apparatus are described herein for enhanced conferencing. A computing device monitor user participation. One or more conference features may be activated or deactivated based on speech patterns of conference participants.