icassp2027-experiment/docs/easycom_egospeak_event_proxy.md

EgoSpeak dense-trajectory event proxy

Status: boundary-centered diagnostic retained for reproducibility. The manuscript's current main behavioral protocol is the matched-opportunity evaluator in easycom_opportunity_event_evaluation.md.

evaluate_egospeak_event_proxy.py provides a deliberately limited bridge between EgoSpeak-compatible dense future-activity predictions and reviewed wearer ENTRY/INT opportunities. It does not claim that wearer voice activity is a semantic floor-event detector.

Score

For every positive event, the default decision region contains the three 5-Hz source rows immediately before its corrected behavior anchor. An independently mined hard negative uses the first three rows of its labeled opportunity. A raw wearer onset/offset relabeled as a hard negative uses the same pre-boundary region as a positive. Regions shorter than 0.6 seconds are excluded and counted; longer annotation evidence is not scored.

At each source row, the script recovers the target-aligned wearer trajectory at 0.6, 0.8, ..., 2.0 seconds. ENTRY uses its mean and INT uses one minus its mean. Max-pooling over the three decision rows produces one score per reviewed opportunity. The output must be called an activity-derived event proxy.

Threshold-free provisional run

This reports AP without fitting thresholds on Sessions 1--3:

python3 evaluate_egospeak_event_proxy.py \
  --reviews reports/easycom_entry_int_sessions1_4_v16/easycom_eot_int_provisional_llm_reviews.jsonl \
  --run egospeak=results/egospeak_miniroada_floor_eval \
  --sessions Session_1 Session_2 Session_3 \
  --allow-provisional-llm-reviews \
  --print-event-examples 5 \
  --output results/easycom_egospeak_event_proxy/sessions1_3_provisional_summary.json

The command writes JSON, a compact metrics CSV, and an event CSV containing the three decision-frame scores and the winning selected-horizon wearer trajectory.

Recall-matched operating points

--matched-recalls on official test sessions additionally requires disjoint Sessions 4--12 reviews and predictions via --calibration-reviews, --calibration-sessions, and optionally --calibration-run. Do not use --allow-in-sample-thresholds for reported results.

The default 0.6-second decision window and 0.6--2.0-second trajectory range remain protocol choices to freeze using training-side data. Report shorter or longer windows only as predeclared sensitivity analyses.