call-disfluency-stress-profiler

Repeated pauses and repairs can be useful prompts for human review, but do not establish a speaker's emotional state or knowledge.

Spoken conversation carries signals that plain transcripts still preserve: disfluency. When a callee is stressed, confused, or overwhelmed, their filled-pause rate (uh, um, er) and self-repair frequency (I mean, actually, word repetitions) spike measurably above baseline. When an agent encounters questions outside its prepared knowledge, the same markers appear in its turns.

This skill reads the finished get_call_run transcript, computes per-side disfluency rates, and flags CALLEE_STRESSED or AGENT_HESITANT when rates cross illustrative, unvalidated demo thresholds. It then offers a reassurance-paced follow-up call goal or a script-review recommendation.

When To Use

• after any CALL-E call where the contact seemed distressed or uncertain
• as part of a QA pipeline to detect agent knowledge gaps at scale
• in healthcare, collections, or support workflows where caller distress carries legal or ethical weight
• to identify topics that consistently cause agent hesitancy (→ update scripts)

When Not To Use

• as a clinical or psychological stress assessment
• during a call; strictly post-call analysis plus pre-call goal crafting
• as the sole basis for medical or legal decisions
• on languages other than English; the lexicon is English-only

Workflow

Audit a finished call

python3 scripts/disfluency_stress_profiler.py analyze \
  --transcript path/to/call-result.json

Reads the real get_call_run result shape or the flat fixture shape. Emits a disfluency card:

• agent_profile / callee_profile: per-side stats including total_words, total_markers, disfluency_rate, and per_turn_counts
• flags[]: CALLEE_STRESSED and/or AGENT_HESITANT when rates exceed their respective thresholds
• verdict: NORMAL / CALLEE_STRESSED / AGENT_HESITANT / BOTH_STRESSED, plus unclear paths
• recommended_action: one of no_action_required, reassurance_followup, review_agent_script, or reassurance_followup_and_script_review
• disclaimer: heuristic advisory disclaimer on every card

Thresholds (defaults)

Side	Threshold	Flag triggered
Callee	8% disfluency rate	CALLEE_STRESSED
Agent	6% disfluency rate	AGENT_HESITANT

Disfluency markers detected

Category	Examples
Filled pauses	uh, um, er, eh, ah, hmm
Self-repairs	I mean, actually, no wait, to rephrase
Repetitions	I I, the the, we we
Hesitation openers	well, , so, , you know, at clause start

Craft the reassurance follow-up goal

python3 scripts/disfluency_stress_profiler.py craft --scenario reassurance-followup

Emits the plan_call inputs JSON whose goal instructs the next call to adopt a calm, unhurried pace, pause after each question, acknowledge concerns explicitly, and ask one question per turn.

Research Background

These references provide conceptual background, not validation of this regex implementation or its 8%/6% defaults. Disfluency has many causes; the labels are advisory review cues, not measured stress or competence.

Research	Relevance
Shriberg, E. — Preliminaries to a Theory of Speech Disfluencies (PhD Thesis, UC Berkeley, 1994)	Gold-standard taxonomy for filled pauses, repetitions, and repairs in spoken dialogue; direct source for the marker categories in this skill
Levelt, W.J.M. — Monitoring and Self-Repair in Speech (Cognition, Vol. 14, 1983, doi:10.1016/0010-0277(83)90026-4)	Theory of self-repair: speakers monitor their own speech and repair when cognitive load is high; repair rate correlates with stress and difficulty
Kumar et al. — Mind the Pause: Disfluency-Aware Objective Tuning for Multilingual Speech Correction with LLMs (ACL 2026, arXiv:2605.12242)	Confirms that disfluency detection from text transcripts is technically feasible with high accuracy; provides methodology basis for lexical marker detection
Ngo et al. — "Mm, Wat?" Detecting Other-initiated Repair Requests in Dialogue (EMNLP 2025)	Studies multimodal repair-initiation detection in Dutch dialogues; it does not validate this skill's stress thresholds
CALL-E Official Documentation — Transcript Structure and get_call_run Result Schema (docs.heycall-e.com)	Defines the exact JSON shapes this skill parses: transcript[].speaker, transcript[].text, and the nested result wrapper

This skill implements a lexical/regex heuristic against the defined marker taxonomy. It does not use model internals and labels every output analysis_mode: "heuristic".

Differences from sibling skills

• call-verbal-irony-detector detects semantic incongruence (sarcasm/irony); this skill measures prosodic-cognitive load signals visible in text.
• call-semantic-barge-in-analyzer measures interruption patterns; this skill measures hesitancy within un-interrupted turns.
• call-agent-certainty-calibrator grades agent fact-statement accuracy; this skill grades conversational fluency and stress level on both sides.