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Align behavioral event annotation protocol

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Xin Wang
2026-09-14 23:43:28 +08:00
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11
docs/easycom_event_review_tool.md
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@@ -24,8 +24,8 @@ python3 prepare_easycom_eot_int_review.py \
The generator emits two independent files: The generator emits two independent files:
- `easycom_eot_int_candidates.jsonl` contains EOT opportunities that pair a - `easycom_eot_int_candidates.jsonl` contains floor-level wearer `0->1`
non-wearer release with a later floor-level wearer `0->1` behavior anchor behavior anchors retrospectively paired with a preceding non-wearer release
(`WEARER_ONSET`, `EOT_OPPORTUNITY_CANDIDATE`), only episode-ending wearer (`WEARER_ONSET`, `EOT_OPPORTUNITY_CANDIDATE`), only episode-ending wearer
`1->0` boundaries where a new non-wearer onset satisfies `1->0` boundaries where a new non-wearer onset satisfies
`wearer_floor_start < other_onset < wearer_offset` and the same activity `wearer_floor_start < other_onset < wearer_offset` and the same activity
@@ -49,9 +49,10 @@ continues across the resumed onset conservatively preserves a new wearer-entry
candidate. Every decision is stored in `temporal_evidence`, including the raw candidate. Every decision is stored in `temporal_evidence`, including the raw
gap, feedback IDs, competing IDs, threshold, and `floor_episode_merged` flag. gap, feedback IDs, competing IDs, threshold, and `floor_episode_merged` flag.
After each non-wearer release, EOT construction scans forward until a For each floor-level wearer onset, EOT construction searches backward for the
floor-level wearer onset or a competing non-wearer onset. Silence length alone most recent non-wearer release and rejects the pair if wearer speech or a
does not invalidate the opportunity. Each primary EOT candidate stores both competing non-wearer onset intervenes. Silence length alone does not invalidate
the opportunity. Each primary EOT candidate stores both
`opportunity_anchor_seconds` and `behavior_anchor_seconds`, plus their response `opportunity_anchor_seconds` and `behavior_anchor_seconds`, plus their response
delay. delay.

218
manuscript/main/main.tex
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@@ -232,21 +232,52 @@ camera-wearer, and aggregate non-wearer activity. Second, the original
participant timeline is retained to derive committed interaction events for participant timeline is retained to derive committed interaction events for
behavioral evaluation. behavioral evaluation.
An \emph{EOT-response} is an observed wearer take-turn after a non-wearer floor \textbf{Behavioral event annotation.}
release; an \emph{INT-yield} is an observed wearer floor release after a We use floor-level wearer transitions as high-recall anchors for positive-event
non-wearer takeover during wearer speech. annotation. Each wearer onset is examined retrospectively for a preceding
non-wearer floor release and labeled as an EOT-response only when the subsequent
wearer speech constitutes a genuine floor take. Similarly, each wearer offset
is examined for a new competing non-wearer onset during the current wearer turn
and labeled as INT-yield only when the competing speaker genuinely takes over
the floor. We use permissive temporal retrieval and let an LLM jointly inspect
participant-resolved timing and transcripts to provide a semantic pre-label,
followed by mandatory human adjudication. Separately, we mine regions with no
wearer response that nevertheless constitute false-positive opportunities:
same-speaker pauses and other-to-other transitions for EOT, and backchannels or
non-takeover overlaps for INT. Only human-confirmed positives and hard negatives
enter evaluation. Appendix~\ref{app:event_annotation} gives the full protocol.
\textbf{Hard-negative region definition.} \begin{table}[t]
Committed positive events and hard-negative regions are derived separately \caption{Behavioral-event annotation statistics after human adjudication.
from the participant-resolved speech timeline. Positive EOT-response and Counts must be filled only after the candidate protocol and annotation set are
INT-yield events are anchored by observed wearer behavior. In contrast, frozen. Scoring-eligible totals include confirmed positives and hard negatives
hard-negative regions do not require any wearer transition. For example, a only.}
mid-turn pause followed by the same non-wearer resuming speech, or an \label{tab:behavior_annotation_stats}
other-to-other floor transfer with the wearer remaining silent throughout, \centering
defines an EOT hard-negative region: a take-turn commitment within this region \small
is counted as a false positive. Likewise, brief backchannels or non-takeover \begin{tabular}{lcc}
overlaps during wearer speech define INT hard-negative regions. Ambiguous or \hline
annotation-corrupted regions are excluded from scoring. Annotation outcome & Train/dev & Test \\
\hline
\multicolumn{3}{l}{\emph{EOT-response}}\\
Positive: EOT-response & \todo{--} & \todo{--} \\
HN: same-speaker pause & \todo{--} & \todo{--} \\
HN: other-to-other transition & \todo{--} & \todo{--} \\
HN: wearer backchannel & \todo{--} & \todo{--} \\
\hline
\multicolumn{3}{l}{\emph{INT-yield}}\\
Positive: INT-yield & \todo{--} & \todo{--} \\
HN: other backchannel & \todo{--} & \todo{--} \\
HN: non-takeover overlap & \todo{--} & \todo{--} \\
HN: wearer holds floor & \todo{--} & \todo{--} \\
\hline
Not applicable & \todo{--} & \todo{--} \\
Excluded / unscorable & \todo{--} & \todo{--} \\
Unresolved & \todo{--} & \todo{--} \\
\textbf{Scoring-eligible total} & \todo{--} & \todo{--} \\
\hline
\end{tabular}
\end{table}
Participant identity is used only to construct these evaluation annotations Participant identity is used only to construct these evaluation annotations
and is never provided to the prediction model. Provided person boxes are used and is never provided to the prediction model. Provided person boxes are used
@@ -518,118 +549,77 @@ do not make the method claim.}
\section{LLM-Assisted Behavioral Event Annotation} \section{LLM-Assisted Behavioral Event Annotation}
\label{app:event_annotation} \label{app:event_annotation}
EasyCom provides participant-resolved speech intervals, transcripts, and EasyCom's raw speech boundaries are acoustic annotations rather than floor
target-of-speech metadata, but raw speech transitions do not uniquely specify labels. We preserve them unchanged for dense forecasting and create a separate
conversational floor function. A wearer onset can be a genuine floor entry, a behavioral-event layer. Its design principle is to anchor an observed wearer
backchannel, or a within-turn resumption; a wearer offset can reflect yielding, behavior first and then retrieve the conversational opportunity that could make
natural completion, or a temporary pause. We therefore construct a separate that behavior meaningful. False-positive opportunities are mined independently
behavioral-event layer while preserving the original speech intervals used for because, by definition, many contain no wearer transition.
dense activity targets. The pipeline has three stages: deterministic candidate
construction, language-model-assisted semantic pre-labeling, and mandatory
human adjudication. The language model never supplies final ground truth.
\subsection{Deterministic candidate construction} \subsection{Positive-event mining}
\textbf{Floor-level wearer entries.} \textbf{Floor-level transitions.}
Raw wearer onsets are conditionally grouped into floor-level episodes. Let Short wearer-silence gaps are conditionally merged before positive mining. In
$g$ be the silence from the preceding wearer offset to a new raw onset. In the the current audit configuration, a raw onset after at most 0.8~s of silence is
current audit configuration, an onset is stored as a treated as a within-turn resumption unless the gap contains sustained competing
\textsc{Wearer-Resume} control rather than proposed as an EOT entry when non-wearer activity. Short feedback-like activity does not by itself split the
$g\leq0.8$~s and the gap contains no plausible competing non-wearer floor wearer episode. This grouping changes only the behavioral-event layer, not the
activity. A non-feedback contribution that occupies at least $0.4$~s of the source intervals or dense targets.
gap, or continues across the resumed wearer onset, conservatively preserves a
new entry candidate. A short ($\leq1.0$~s) feedback-like lexical or nonverbal
contribution is retained as evidence but does not by itself split the wearer
episode. These rules group events only; they do not modify the source
annotations.
Following each candidate non-wearer release, the generator opens an EOT \textbf{EOT-response.}
opportunity and scans forward until a floor-level wearer entry or a competing Starting from a floor-level wearer onset at $t_b$, we search backward for a
non-wearer onset terminates it. A retained \textsc{Wearer-Onset} candidate plausible non-wearer floor release at $t_r$. A candidate is retained only when
therefore contains two immutable anchors: the non-wearer release the interval from $t_r$ to $t_b$ contains neither intervening wearer speech nor
(\emph{opportunity anchor}) and the subsequent floor-level wearer onset a competing non-wearer onset. The record stores both $t_r$ as the
(\emph{behavior anchor}). The response delay is stored explicitly. Silence \emph{opportunity anchor} and $t_b$ as the \emph{behavior anchor}, together with
duration alone does not cancel the opportunity; an unpaired wearer onset is the response delay $t_b-t_r$. Silence duration alone does not reject a
kept only as an audit control and does not enter the primary EOT queue. candidate. The LLM and human annotator then decide whether the release is
conversationally valid and whether the wearer speech is a genuine floor take.
Unpaired wearer onsets remain audit controls.
\textbf{EOT hard negatives.} \textbf{INT-yield.}
Two participant-resolved timing rules independently propose regions in which Starting from an episode-ending wearer offset $t_y$, we search backward inside
the wearer should remain silent. A \textsc{Same-Speaker-Pause} region is the current wearer episode for a new competing non-wearer onset $t_o$. With
created when a non-wearer resumes within 2~s and both the wearer and all other $t_w$ denoting the wearer-episode start, an INT candidate must satisfy
participants remain silent in the intervening gap. An
\textsc{Other-to-Other} region is created when a different non-wearer begins
within 1.5~s, the original speaker does not first resume, and the wearer stays
silent through the handover. The semantic pre-labeler can additionally assign
\textsc{EOT-Wearer-Backchannel} to a floor-level onset that is interpretable
as feedback rather than a response.
\textbf{INT candidates and hard negatives.}
Let $t_w$ be the start of the current floor-level wearer episode, $t_o$ a new
non-wearer onset, and $t_y$ the episode-ending wearer offset. A potential
\textsc{INT-Yield} is proposed only when
\begin{equation} \begin{equation}
t_w < t_o < t_y < t_o^{\mathrm{off}}, t_w < t_o < t_y < t_o^{\mathrm{off}}.
\label{eq:int_candidate} \label{eq:int_candidate}
\end{equation} \end{equation}
with a one-frame (50-ms) tolerance only at the offset boundary in the current The current audit allows a one-frame (50-ms) tolerance only at the offset
audit. Thus the competing activity must begin after the wearer acquired the boundary. Pre-existing other activity, post-completion onsets, offsets inside a
floor and must continue across the wearer yield. Pre-existing non-wearer merged wearer episode, and completion-to-silence remain controls. Timing only
activity, post-completion onsets, offsets internal to merged wearer episodes, retrieves a possible interruption; the LLM and human annotator decide whether
and offsets without crossing activity are retained separately as the competitor genuinely takes over and the wearer yields.
\textsc{Wearer-Offset-Control} records and are not sent for semantic
pre-labeling. Thus ordinary completion-to-silence cases do not enter the
primary INT review queue.
INT hard-negative regions are also proposed without requiring a wearer \subsection{False-positive opportunity mining}
offset. If a non-wearer contribution ends while the wearer remains active
throughout it and for at least another 0.4~s, the region is labeled by rule as
\textsc{INT-Other-Backchannel} when it is short and feedback-like, and
\textsc{INT-Non-Takeover-Overlap} otherwise. These timing labels are candidate
priors, not final semantic annotations.
\subsection{Semantic pre-labeling} Hard-negative regions are retrieved independently of positive wearer
transitions. For EOT, a same-speaker pause followed by resumption and an
other-to-other handover while the wearer stays silent are false-entry
opportunities. For INT, a short non-wearer backchannel or a non-takeover overlap
during which the wearer retains and continues the floor is a false-yield
opportunity. The timing rules provide candidate types, not final semantic
labels.
For every primary candidate, the annotation assistant receives the candidate \subsection{Semantic pre-labeling and human adjudication}
source and rule type, immutable timestamps, participant-resolved activity,
neighboring onsets and offsets, gap and overlap measurements, transcript
context, and target-of-speech metadata when available. Participant identities
are used only inside this annotation package. The assistant is instructed to
treat timing as factual, not to invent prosody, gaze, gesture, intention, or
other unavailable cues, and not to equate onset with floor entry, offset with
yield, silence with EOT, or overlap with interruption. EasyCom's \texttt{[L]}
marker is treated as evidence of laughter only: it is not by itself a sustained
floor take or takeover.
The allowed decisions are \textsc{Positive}, \textsc{Hard-Negative}, The annotation assistant jointly receives immutable timestamps,
\textsc{Not-Applicable}, \textsc{Exclude}, and \textsc{Unsure}. Positive participant-resolved activity, neighboring boundaries, gap and overlap
events are typed as \textsc{EOT-Response} or \textsc{INT-Yield}; hard measurements, transcripts, and target-of-speech metadata when available. It is
negatives use a fixed subtype vocabulary. The current pre-labeling run uses instructed not to equate onset with entry, offset with yield, silence with EOT,
\texttt{deepseek-v4-flash}, temperature zero, and two passes: a primary pass or overlap with interruption, and not to invent unavailable prosody, gaze, or
and a skeptical pass that seeks alternative explanations. If they disagree on intent. EasyCom's \texttt{[L]} marker supplies laughter evidence only and is not
the verdict, event type, or hard-negative subtype, the combined suggestion is by itself a sustained floor take or takeover.
\textsc{Unsure}. Model identifier, prompt version, per-request prompt hashes,
both raw decisions, confidence values, and explanations are stored with every
candidate.
\subsection{Human adjudication and scoring eligibility} We use \texttt{deepseek-v4-flash} at temperature zero with a primary and a
skeptical pass. Disagreement on verdict, event type, or hard-negative subtype
Every AI-pre-labeled candidate enters an event-centered interface containing produces an \textsc{Unsure} suggestion. Every candidate then receives mandatory
the original video, local participant timeline, transcript, deterministic human adjudication: accept the complete pre-label, relabel it, or exclude an
timing evidence, and the AI suggestion. The adjudicator performs one primary unscorable region. Only human-confirmed \textsc{EOT-Response},
action: accept the complete AI label, relabel it, or exclude an unscorable \textsc{INT-Yield}, and hard-negative opportunities enter evaluation;
sample. Relabeling exposes the same constrained verdict, event-type, and \textsc{Not-Applicable}, \textsc{Exclude}, and unresolved candidates do not.
hard-negative subtype vocabulary. A confidence level is required; notes are Annotation-only transcripts, identities, target-of-speech metadata, LLM output,
optional, and an optional $\pm0.2$~s boundary correction is available only for and future observations are never supplied to the evaluated forecasting model.
an evident source-annotation error. The original event time is always retained.
Only the completed human decision is eligible to become semantic ground truth.
\textsc{Not-Applicable} denotes a valid observation outside the committed
EOT/INT task, whereas \textsc{Exclude} denotes a region that cannot be scored
reliably because of annotation corruption, a recording boundary, missing
context, timestamp corruption, or another unresolved defect. Neither category
enters the primary event denominator. Transcripts, participant identities,
target-of-speech metadata, annotation-model outputs, and future observations
are never inputs to the evaluated forecasting model.
\todo{Before freezing this annotation protocol, audit randomly sampled \todo{Before freezing this annotation protocol, audit randomly sampled
continuous timeline segments without candidate hints to estimate candidate continuous timeline segments without candidate hints to estimate candidate

23
prepare_easycom_eot_int_review.py
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@@ -422,18 +422,21 @@ def derive_protocol_candidates(utterances: Sequence[Utterance], clips: Sequence[
reason="short raw wearer pause without sustained competing other takeover; merged into the existing floor episode", reason="short raw wearer pause without sustained competing other takeover; merged into the existing floor episode",
), ("WEARER_RESUME", frame, ())) ), ("WEARER_RESUME", frame, ()))
# A1: open an opportunity at a non-wearer release and pair it with the # A1: anchor the observed floor-level wearer entry, then retrieve the
# subsequent floor-level wearer entry only if no competing other onset # most recent preceding non-wearer release. Keep the pair only if no
# or wearer activity intervenes. Long silence alone does not invalidate # competing other onset or wearer activity intervenes. Long silence
# the opportunity. Unpaired floor onsets are retained as controls. # alone does not invalidate the opportunity. Unpaired floor onsets are
# retained as controls.
paired_floor_onsets: set[int] = set() paired_floor_onsets: set[int] = set()
for release in other_rows: for onset_frame in floor_onsets:
opportunity_frame = release.dense_end_frame_exclusive releases = [
onset_frame = next( row for row in other_rows
(value for value in floor_onsets if value >= opportunity_frame), None if row.dense_end_frame_exclusive <= onset_frame
) ]
if onset_frame is None: release = max(releases, key=lambda row: row.dense_end_frame_exclusive, default=None)
if release is None:
continue continue
opportunity_frame = release.dense_end_frame_exclusive
wearer_silent = ( wearer_silent = (
opportunity_frame == onset_frame opportunity_frame == onset_frame
or _all_state( or _all_state(