call-cross-lingual-emotion-preservation

When the message crosses a language, does the urgency survive?

language-bridge-call relays a request across languages in two legs. This skill is an experimental QA companion: it compares English urgency-marker levels in supplied text from both legs. It does not measure actual emotion or prove that a translation preserved it. A phrase such as "today, immediately, please" that arrives as "sometime this week would be fine" is a failed relay even when the words are translated correctly.

When To Use

• after a language-bridge-call relay, to check the requester's urgency survived the second leg
• after any translated CALL-E call where emotional subtext matters (escalations, care requests, time-critical arrangements)
• to generate an intensity-calibrated relay goal for the next plan_call

When Not To Use

• to relay or translate anything; use language-bridge-call
• to detect sarcasm or stated-vs-meant mismatch; use call-verbal-irony-detector
• to audit the relay callee's own emotions; only the relay agent's expressed intensity is measured, because the relay speaks on the requester's behalf
• on non-English source OR relay text; the same English-only lexicon scores both. Provide operator-prepared English translations when needed; this script does not translate. A target-language parameter does not change the scorer. Non-English text can produce misleading low scores.

Workflow

Analyze a relay

python3 scripts/emotion_preservation.py analyze --source-context path/to/source.json --relay-transcript path/to/relay.json

--source-context accepts the requester's call-result JSON (callee turns are used) or a plain-text operator note. --relay-transcript accepts the relay leg's CALL-E result (nested get_call_run or flat fixture shape). Both texts must be English or separately translated into English by the operator. The script does not detect or enforce language eligibility. Emits a card:

• source_intensity / relay_intensity: {score, level, markers}; the relay side counts AGENT turns only
• drift: FLATTENED / PRESERVED / AMPLIFIED (level comparison)
• parity_score: 1.0 equal, 0.5 adjacent, 0.0 two steps apart
• evidence: masked spans with matched markers, from both sides
• emotion_assessment: "unclear" with a reason when the source context is empty or the relay has no agent turns
• recommended_action: re_relay_with_calibrated_goal (with the goal text calibrated to the SOURCE intensity) or proceed

These are legacy action labels for human review. Scores and low/medium/high levels are illustrative lexicon thresholds, not empirically calibrated emotion measures. Neither proceed nor a re-relay suggestion authorizes a new call, emergency response or other consequential action.

Craft the calibrated relay goal

python3 scripts/emotion_preservation.py craft --scenario emotion-relay --intensity high --language en

Emits the plan_call inputs JSON whose goal is the same intensity- calibrated template the card recommends on drift (high / medium / low).

Scientific Foundation

Research	Relevance
ZEST: Zero Shot Audio to Audio Emotion Transfer With Speaker Disentanglement (ICASSP 2024, arXiv 2401.04511)	Zero-shot emotion transfer between speakers; motivates intensity-preserving relay design
EELE: Exploring Efficient and Extensible LoRA Integration in Emotional Text-to-Speech (2024, arXiv 2408.10852)	Efficient emotional TTS control; motivates mapping intensity levels to concrete phrasing guidance

Both papers build neural emotion-transfer systems; this skill deliberately implements a lexical intensity mapper on transcripts only and labels every output analysis_mode: "heuristic" - it is informed by that research, not an implementation of it.

Differences from sibling skills

• language-bridge-call performs the relay; this skill audits the relay's emotional fidelity and calibrates the next attempt.
• call-semantic-barge-in-analyzer profiles how the callee participated; this skill measures what the relay agent expressed on someone's behalf.