Description
Description
Advance beyond default diarization by solving the problems that break search quality: overlap and mis-segmentation. You will tune voice activity detection thresholds for quiet rooms versus field recordings with traffic and music. We cover spectral clustering and embedding-based methods, then show practical heuristics that reduce identity swaps. Labs teach resegmentation using ASR alignment to fix late/early boundaries that scramble snippets. You will learn to detect crosstalk and mark overlapping speech with separate layers for accurate playback. A calibration section demonstrates how a few minutes of hand-labeled audio can dramatically improve clusters. We discuss privacy-sensitive labeling and when to use anonymous speaker IDs versus known names. Metrics like DER, JER, and confusion matrices are explained with visual dashboards you can reuse. By the end, speaker turns look natural, search results quote the right voice, and editors trust the timeline.
Format
Expert videos, annotated audio sets, resegmentation notebooks, dashboard templates, parameter cheat-sheets
Duration
4 hours + optional deep-dive labs
What You’ll Learn
– VAD tuning
– Clustering methods
– ASR-aligned resegmentation
– Overlap detection layers
– DER/JER diagnostics
– Privacy-aware labeling
Target Audience
Engineers and archivists who need dependable speaker boundaries for search and editing
Videos + annotated audio + notebooks + dashboards + cheat-sheets
4 hours
– VAD tuning
– Clustering
– Resegmentation
– Overlap layers
– DER/JER
– Privacy labeling
Engineers, archivists, technical editors
English