Virtual Meeting Speaker Separation Through Iterative Audio Clustering

The patent segments the speaker separation task into multiple processing stages: initial clustering to group audio segments by speaker characteristics, iterative refinement to separate overlapping speech, and post-processing to handle unbalanced talk times. This multi-stage segmentation approach improves separation accuracy by breaking down the complex problem into manageable steps while systematically addressing each challenge (unknown speaker numbers, environmental variability, overlapping speech, and gender differences).

The system employs dynamic adaptive processing that adjusts separation parameters based on real-time analysis of speaker characteristics, environmental conditions, and speech overlap patterns. The iterative refinement process dynamically modifies clustering parameters and separation thresholds to optimize performance for each specific conferencing scenario, thereby improving reliability without requiring a fixed complex structure.

The patent implements preliminary action by performing initial speaker clustering and environmental characterization before the main separation process. Audio segments are pre-grouped by speaker characteristics and environmental conditions, creating organized data structures that accelerate subsequent iterative refinement. This preliminary organization reduces the computational burden during real-time processing while maintaining high identification accuracy.

The system applies partial action by focusing computational resources on the most challenging aspects of speaker separation - specifically targeting overlapping speech segments and boundary cases where speakers transition. Rather than uniformly processing all audio segments with maximum complexity, the system selectively applies advanced algorithms only where needed, reducing overall processing time while maintaining precision for difficult cases.