NLP Speech Timing Prediction via Latency and Voice Pattern Analysis
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Solution Overview
Problem
Existing video conferencing systems face challenges in determining when participants can speak due to latency issues, leading to interruptions and inefficiencies, as human speech timing is disrupted by geographic and system delays.
Innovation Solution
A computer-implemented method using natural language processing (NLP) to predict when a participant can speak by analyzing latency and voice patterns, providing visual or aural cues to indicate when it is acceptable to speak, by training a machine learning model to learn and monitor voice patterns and latency metrics.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If video conferencing is used with geographic distribution, then accessibility and collaboration reach are improved, but latency causes speech timing disruptions and interruptions
Solution Approach 1:
The system performs preliminary analysis of speech patterns and latency metrics before the actual speech event occurs. By pre-training machine learning models on voice patterns and pre-calculating latency compensation values, the system prepares timing predictions in advance, allowing participants to speak at the optimal moment without interruptions despite geographic distance.
Solution Approach 2:
The system continuously monitors speech events, latency measurements, and pattern recognition outcomes, then feeds this information back to refine predictions. The machine learning models are retrained with new data from each conference session, improving the accuracy of speech timing predictions over time and compensating for varying network conditions.
2Measurement precision
If machine learning models are trained iteratively for each participant, then speech pattern recognition accuracy is improved, but computational complexity and processing time increase
Solution Approach 1:
The system divides the machine learning process into separate models for each participant rather than using a single centralized model. Each participant's speech patterns are analyzed independently, allowing the system to focus computational resources on individual patterns and reduce the overall complexity of training and processing.
Solution Approach 2:
Each participant's data serves to train their own personalized model, and the model automatically improves through iterative learning during conference sessions. The system self-optimizes by continuously retraining models with new speech data without requiring external intervention or manual configuration.
3Loss of time
If latency compensation is implemented for each participant, then speech timing accuracy is improved, but system processing overhead increases
Solution Approach 1:
Latency compensation values are calculated in advance based on network measurements and participant locations before conferences begin. These pre-computed values are stored and applied automatically during speech events, eliminating the need for real-time latency calculation and reducing processing overhead during actual conferencing.
Solution Approach 2:
The system dynamically adjusts latency compensation parameters based on measured network conditions, participant speech patterns, and historical data. By changing these parameters iteratively and storing optimized values, the system adapts to varying network conditions without requiring complex real-time recalculations.
Data Source
AI summary
A computer-implemented system, method, and computer program product are described that predicts a time in which a participant of a group of participants may speak in a group meeting. The prediction is based in part on latency between each participant in the group of participants and learned voice patterns of each respective participant. Once a prediction is determined, a cue to speak or remain silent is provided as one of a visual or aural cue to the participant to speak during the group meeting. Predicting a time in which the participant of a group of participants may speak includes training a lingual processing model iteratively for each respective participant to learn voice patterns of participants.