Synthetic Counterparty Detector

The call-synthetic-counterparty-detector skill computes a heuristic score from supplied metadata and text. It does not extract audio features, implement PDSM, authenticate identity, or switch protocols. Its recommendation is an experimental hint for human review, not a calibrated probability or permission to bypass safeguards.

Research Scope

The latency, duration and filler-word thresholds are demonstration choices, not a reproduced or validated deepfake model. See research scope.

How it works

The detector ingests the call transcript and a metadata payload containing acoustic telemetry:

1. Micro-latency variance: Checks if the turn-taking latency is too consistent (variance < 500ms). Humans naturally vary their response times based on cognitive load.
2. Zero-breath phrasing: Checks if the caller spoke for an impossibly long duration (e.g. >15 seconds) without a biological pause (PDSM anomaly).
3. Linguistic determinism: Checks if the caller uses zero filler words ("um", "ah") over a long, complex sequence of speech.

If the combined confidence score is >= 0.7, it flags SYNTHETIC_COUNTERPARTY = True and recommends SWITCH_TO_M2M_PROTOCOL.

Expected Outcomes & Metrics

Metric	Target	Notes
Detection Accuracy	> 92% aspiration	Unvalidated target; no TTS benchmark is supplied.
False Positive Rate	< 2% aspiration	Unvalidated target; false classifications are possible.

Limitations & Known Constraints

• Metadata Dependency: This skill relies entirely on the telephony provider (e.g., Twilio, Deepgram) supplying accurate turn latency and speech duration metadata.
• Evolving AI: As TTS systems introduce artificial breath sounds and randomized latencies, the heuristic thresholds must be continuously updated.