# EasyCom wearer-centric EOT/INT pre-label and review pipeline The workflow has three explicit stages. Speech activity timestamps are read-only throughout; reviewed events never modify the dense 5 Hz activity ground truth. ```text EasyCom participant speech intervals -> floor-episode grouping + rule candidates + structured timing evidence -> DeepSeek semantic pre-label (optional but recommended) -> human Accept / Relabel / Exclude ``` ## 1. Generate candidates Develop the rules on training-side Sessions 4--12: ```bash python3 prepare_easycom_eot_int_review.py \ --dataset-root /path/to/easycom \ --sessions Session_4 Session_5 Session_6 Session_7 Session_8 Session_9 Session_10 Session_11 Session_12 \ --output-dir reports/easycom_floor_event_candidates ``` The generator emits two independent files: - `easycom_eot_int_candidates.jsonl` contains floor-level wearer `0->1` behavior anchors with one or more admissible preceding non-wearer releases (`WEARER_ONSET`, `EOT_OPPORTUNITY_CANDIDATE`), only episode-ending wearer `1->0` boundaries where a new non-wearer onset satisfies `wearer_floor_start < other_onset < wearer_offset` and the same activity crosses the offset (`WEARER_OFFSET`, `YIELD_CANDIDATE`), and hard-negative regions that need no wearer transition: `SAME_SPEAKER_PAUSE`, `OTHER_TO_OTHER`, `OTHER_BACKCHANNEL`, and `NON_TAKEOVER_OVERLAP`; - `easycom_eot_int_controls.jsonl` contains short within-turn raw onsets (`WEARER_RESUME`), floor-level wearer entries with no surviving preceding release opportunity (`WEARER_ONSET_CONTROL`), and wearer offsets that fail the strict INT ordering/crossing prerequisite (`WEARER_OFFSET_CONTROL`). They are retained for audits, but are not sent to DeepSeek or placed in the primary human-review queue. A raw wearer onset is merged into its preceding floor episode only when its preceding silence is at most 0.8 seconds and no plausible sustained non-wearer takeover occurs in that gap. Short feedback-like lexical or nonverbal activity is recorded but does not by itself split the episode. A non-feedback other contribution that occupies at least `continuation_min_seconds` of the gap or continues across the resumed onset conservatively preserves a new wearer-entry candidate. Every decision is stored in `temporal_evidence`, including the raw gap, feedback IDs, competing IDs, threshold, and `floor_episode_merged` flag. EOT construction iterates floor-level wearer onsets, then searches backward for preceding non-wearer releases. A release is an admissible option only when the wearer stays silent and no new non-wearer onset or already-active other floor invalidates the interval before the wearer onset. Silence length alone does not invalidate an option. Each wearer onset produces at most one primary row. Its `opportunity_options` list contains the only release anchors that DeepSeek or a human may select; neither may invent a timestamp. The selected release is the opportunity anchor, the wearer onset is the behavior anchor, and their interval deterministically defines the valid EOT prediction interval. Paired wearer onsets and strictly qualified offsets are initially `unsure`; timing alone is not a semantic positive label. The INT prerequisite is `wearer_floor_start < other.start < wearer_offset < other.end`, with a configurable one-frame (50 ms) tolerance only at the offset boundary. Pre-existing other activity and raw offsets inside a merged wearer episode stay in the controls file. Hard-negative scans are rule hints and still require AI and human review. The JSONL retains candidate source, original event time, participant-level activity, raw durations/gaps/overlap evidence, transcript, and `Target_of_Speech` when present. `Target_of_Speech` is annotation assistance only and cannot be a deployable model input. All heuristic durations are configurable rather than frozen paper rules: ```text --relation-window-seconds --same-speaker-pause-max-seconds --other-handover-max-seconds --short-vocalization-max-seconds --continuation-min-seconds --int-offset-tolerance-seconds --wearer-resume-merge-max-seconds ``` Use `--max-candidates 1` for a one-event smoke test. `summary.json` records the exact configuration and candidate counts. `easycom_session_timelines.json` stores the read-only participant speech lanes used by the full-session web timeline; it is not sent to DeepSeek. ## 2. Add DeepSeek pre-labels Prompt v7 treats the EasyCom transcript marker `[L]` as laughter-only evidence, not as a lexical floor take. Wearer laughter during another participant's retained turn is directed toward `EOT_WEARER_BACKCHANNEL`; non-wearer laughter overlapping a retained wearer turn is directed toward `INT_OTHER_BACKCHANNEL`. Ambiguous laughter remains `UNSURE`, and `[L]` alone is neither an exclusion reason nor evidence of a visible smile. The prompt also treats the two EOT anchor options and strict INT ordering as deterministic prerequisites. For a positive EOT response it must return `selected_opportunity_uid` from the supplied options; all other decisions use `null`. `needs_human_review` is not requested from DeepSeek: every pre-label enters the human queue by protocol, so the adjudicator assigns that boolean locally. This prevents a string-valued model field from invalidating and retrying an otherwise valid batch. First inspect exactly what would be uploaded: ```bash python3 adjudicate_easycom_eot_int_candidates.py \ --input reports/easycom_floor_event_candidates/easycom_eot_int_candidates.jsonl \ --output reports/easycom_floor_event_candidates/easycom_eot_int_deepseek.jsonl \ --model deepseek-v4-flash \ --max-events 1 \ --dry-run ``` After setting `DEEPSEEK_API_KEY` and obtaining authorization to upload the candidate excerpts: ```bash python3 adjudicate_easycom_eot_int_candidates.py \ --input reports/easycom_floor_event_candidates/easycom_eot_int_candidates.jsonl \ --output reports/easycom_floor_event_candidates/easycom_eot_int_deepseek.jsonl \ --model deepseek-v4-flash \ --allow-external-transcript-upload ``` DeepSeek sees candidate source, structured timing, per-speaker activity, and transcript together. It returns one of `POSITIVE`, `HARD_NEGATIVE`, `NOT_APPLICABLE`, `EXCLUDE`, or `UNSURE`; positives additionally receive `EOT_RESPONSE` or `INT_YIELD`, and hard negatives receive a named subtype. It cannot modify timestamps. The output preserves the original rule candidate, the selected EOT opportunity UID when applicable, and the complete AI decision/provenance. During live adjudication, the CLI displays a dependency-free progress bar over all candidate/pass work, including the current pass, batch, percentage, and elapsed time. Redirected logs emit periodic progress lines instead of carriage return animation. As defense in depth, the adjudicator also detects and skips non-crossing `WEARER_OFFSET` rows from older v2 candidate files before making any API call. ## 3. Human review Run the web workspace on the AI-enriched JSONL: ```bash python3 review_floor_events.py \ --dataset-root /path/to/easycom \ --events reports/easycom_floor_event_candidates/easycom_eot_int_deepseek.jsonl \ --output reports/easycom_event_reviews/reviews.jsonl ``` Open `http://127.0.0.1:8765`. The page shows a session candidate overview, participant activity near the event, video/audio, transcript, structured timing, and the AI verdict/confidence/reason. A decision guide first states the behavioral question, then presents the exact AI verdict/subtype with a plain-language meaning; the Accept control repeats that exact label so its effect is explicit. The common path is one operation: - **Accept** keeps the AI verdict/type and its selected EOT opportunity anchor; - **Relabel** expands verdict and subtype controls and, for a positive EOT, requires one of the supplied release anchors; - **Exclude** requires a concrete data-quality reason. The current primary-review UI deliberately has no second-review action. Older saved `second_review` records remain readable for backward compatibility. Boundary correction is hidden under an optional disclosure and is limited to one 200 ms bin. The original timestamp is always retained. Each save atomically rewrites JSONL and CSV while preserving the rule candidate, AI decision, human decision, relabel flag, exclusion state, confidence, and provenance. The current UI does not ask for a reviewer name; the export retains an empty `reviewer` field only for compatibility with older review files. The primary review queue also filters legacy non-crossing `WEARER_OFFSET` rows and explicit `WEARER_RESUME` controls. To audit the controls intentionally, pass `--events .../easycom_eot_int_controls.jsonl --include-controls` and use a separate review output file. The old `--include-offset-controls` spelling remains an alias. Protocol wording for the paper appendix: > Raw wearer speech onsets are first grouped into floor-level speech episodes > by merging short within-turn resumptions that are not separated by a > competing floor takeover. Only the resulting floor-level 0→1 transitions are > proposed as EOT-response candidates. > The original speech intervals are preserved unchanged for dense forecasting > and auditability; this grouping is used only for behavioral event annotation. ## Freeze discipline Develop the guide and resolve disagreements only on Sessions 4--12. Freeze categories and thresholds before reviewing official test Sessions 1--3. Use two independent human review files plus a separate adjudication output for paper- quality labels. DeepSeek is pre-label metadata, not an independent reviewer.