Run Accent Testing

Set up and launch a Coval accent testing sweep end-to-end: one voice agent, one test set, an accent persona pack, shared metrics, a per-persona comparison table built from the results, and a saved multi-run report grouped by Persona (created for you via the API). Mirrors the public cookbook at https://docs.coval.dev/guides/testing-across-accents.

Unlike the audio-quality pack, the accent personas are not built-in. This skill creates them in the user's organization, one per accent voice, and makes each one mirror the org's Standard Customer behavior so the only variable between runs is the speaker's accent.

When the runs complete this skill hands off to analyze-accent-report for the recommended-fixes write-up.

When To Use

• The user wants to follow the "Testing Across Accents" cookbook but does not want to hand-create personas and hand-launch each run.
• The user has a voice agent (PSTN, SIP, WebSocket, LiveKit, Vapi, OpenAI Realtime, Gemini Realtime, etc.) and a test set they trust.
• The user wants a saved multi-run report grouped by Persona (created via the API) that they can open, share, or pass to the analysis skill.

Do not use this skill for chat-only agents. Accent testing exercises speech recognition (STT) and audio timing, none of which a chat agent runs.

Prerequisites

coval --version    # CLI must be installed (brew install coval-ai/tap/coval)
coval whoami       # must be authenticated to the right org

If coval whoami fails, run coval login first.

Accent Persona Pack

The cookbook compares one neutral baseline against six accent personas. Each accent persona uses a distinct accent voice; every other setting mirrors the baseline so differences come from the accent, not behavior.

Persona Name	--voice	Accent
Standard Customer	(built-in baseline)	Neutral baseline
Indian Accent (Vidya)	vidya	Indian
German Accent (Marshal)	marshal	German-accented
Chinese Accent (Ziyu)	ziyu	Chinese-accented
US Southern Accent (Cletus)	cletus	US Southern
Nigerian Accent (Kehinde)	kehinde	Nigerian
Malaysian Accent (Darryl)	darryl	Malaysian

Low concurrency is expected. All six accent voices are lower-concurrency ElevenLabs voices, and ElevenLabs voice simulations are globally capped per organization. Launch these runs at a low --concurrency (2 is a safe default) and expect simulations to queue and finish more slowly than a standard sweep. This is normal for this suite — it is not a failure.

Workflow

Step 1: Confirm The Voice Agent

If $ARGUMENTS includes an agent name or ID, look it up. Otherwise list voice agents and ask the user to pick one:

coval agents list --format json | jq '.[] | select(.model_type | test("VOICE|REALTIME|WEBSOCKET")) | {id, display_name, model_type}'

Pick exactly one. The cookbook is explicit: keep the agent configuration fixed across all runs. Confirm with the user before continuing.

If the agent emits OpenTelemetry traces, recommend including trace-based STT and timing metrics in step 3. If not, recommend setting up traces (see Coval traces docs) or proceeding with transcript-and-audio-only metrics such as Transcription Error.

Step 2: Confirm The Test Set

coval test-sets list

Pick one test set. The same test set must be used across every persona run. Subsampling is allowed as long as the same sampled cases run across all seven personas — use a fixed --sub-sample-seed (e.g. 42) and the same --sub-sample-size on every run.

Step 3: Confirm Metrics

Accent testing is primarily a speech-recognition stress test: does the agent correctly understand accented speech, and does any mis-recognition change the task outcome? Lead with STT, then task outcome and call shape. Reuse any metrics the agent already has attached when they fit.

Goal	Useful Metrics
Speech recognition (headline)	Transcription Error (audio-derived, needs no traces) is the default; add STT Word Error Rate only after confirming the agent emits STT trace spans and the metric actually scores — otherwise it silently fails
Task outcome	Composite evaluation, task-completion LLM judges, or scenario-specific pass/fail metrics
Responsiveness	Latency, time to first audio, trace TTFB, provider response-time
Conversation flow	Turn count, repeated confirmation/clarification loops, early termination, silence, interruption rate

Do not include manager_audio_frequency — it measures signal characteristics, not accent comprehension.

Do not recommend STT Word Error Rate (Audio Upload) for this general workflow. It only applies when the test set uses uploaded audio with reference transcripts, which is a different setup from the normal voice-simulation sweep.

STT Word Error Rate needs the right trace spans, or it silently fails. The trace-based STT Word Error Rate metric only computes when the agent's OpenTelemetry traces carry STT spans with both the recognized hypothesis and a reference value. Many voice stacks — Vapi/PSTN agents in particular — do not emit those spans, in which case this metric comes back FAILED/empty on nearly every simulation. That is a trace-setup gap, not an accent finding. Always include Transcription Error (audio-derived word error rate, needs no traces) as the reliable recognition headline, and only rely on STT Word Error Rate after you have confirmed it scores on a real run for this agent.

Recognition that works without agent traces (the common case — most agents, Vapi/PSTN/Pipecat included, emit no STT spans) is Transcription Error: a Coval built-in that re-transcribes the recording with its own STT and scores word error rate, so it needs no traces. It is a built-in (organization_id=NULL) and can be paged out of a plain coval metrics list, so resolve it by name — note the metrics filter uses =, not the : personas use:

# Recognition metric that needs no traces — always attach this.
TRANSCRIPTION_ERROR_ID=$(coval metrics list --include-builtin --page-size 100 \
  --filter 'metric_name="Transcription Error"' --format json \
  | jq -r '.[] | select(.metric_name == "Transcription Error") | .id' | head -1)

# Browse task-outcome / responsiveness / flow metrics; reuse the agent's
# attached metrics when they fit.
coval metrics list --include-builtin

Build METRIC_IDS as a comma-separated string with Transcription Error first, then the task-outcome and call-shape metric IDs you chose. Always attach a working recognition metric — measuring recognition is the entire point of an accent sweep. Do not just omit --metric-ids to fall back on the agent's defaults: many agents have no metrics attached (or none that measure recognition), so the sweep would score everything except the thing it exists to test. Add STT Word Error Rate only in addition (never instead), and only after confirming the agent emits STT trace spans and it actually scores.

Step 4: Ensure The Accent Personas Exist

The accent personas are not built-in, so create any that are missing. Make each one mirror the org's Standard Customer so the only variable is the accent voice.

First resolve Standard Customer and read its behavior prompt and wait time so the accent personas can mirror them:

STD_ID=$(coval personas list --filter 'name:"Standard Customer"' --format json \
  | jq -r '.[] | select(.name == "Standard Customer") | .id' | head -1)

STD_PROMPT=""
STD_WAIT=""
if [ -n "$STD_ID" ]; then
  STD=$(coval personas get "$STD_ID" --format json)
  STD_PROMPT=$(echo "$STD" | jq -r '.persona_prompt // ""')
  STD_WAIT=$(echo "$STD"   | jq -r '.wait_seconds // ""')
fi

# Fallbacks if Standard Customer is missing or has no prompt/wait time.
if [ -z "$STD_PROMPT" ] || [ "$STD_PROMPT" = "null" ]; then
  STD_PROMPT="You are a standard customer calling in with a routine request. Speak naturally and conversationally. Answer the agent's questions, provide the information it asks for, and work toward completing your task."
fi
if [ -z "$STD_WAIT" ] || [ "$STD_WAIT" = "null" ]; then
  STD_WAIT="0.5"
fi

# IMPORTANT: do NOT reuse Standard Customer's language code for the accent
# personas. Accent voices are locale-bound and most do NOT accept en-US, so
# `coval personas create` rejects the pair with
# "Voice 'X' does not support language 'Y'". Supported languages per voice:
#   vidya  en, en-IN     marshal en, en-GB    ziyu   en, en-GB
#   cletus en, en-US     kehinde en (only)    darryl en (only)
# The one language all six accent voices share is the base "en", so use "en" for
# every accent persona. A single shared language keeps the accent voice the only
# variable. (If you specifically want an accent-native locale, map each voice to
# a code it supports above instead — but then language co-varies with accent.)
ACCENT_LANG="en"

Then create each accent persona only if an exact-name match does not already exist (idempotent — never create duplicates):

# "Persona Name|voice" — the voice is the Coval catalog name.
ACCENTS=(
  "Indian Accent (Vidya)|vidya"
  "German Accent (Marshal)|marshal"
  "Chinese Accent (Ziyu)|ziyu"
  "US Southern Accent (Cletus)|cletus"
  "Nigerian Accent (Kehinde)|kehinde"
  "Malaysian Accent (Darryl)|darryl"
)

for entry in "${ACCENTS[@]}"; do
  name="${entry%%|*}"
  voice="${entry##*|}"

  existing=$(coval personas list --filter "name:\"$name\"" --format json \
    | jq -r --arg n "$name" '.[] | select(.name == $n) | .id' | head -1)

  if [ -n "$existing" ]; then
    echo "exists: $name ($existing)"
    continue
  fi

  echo "creating: $name (voice: $voice)"
  coval personas create \
    --name "$name" \
    --voice "$voice" \
    --language "$ACCENT_LANG" \
    --prompt "$STD_PROMPT" \
    --wait-seconds "$STD_WAIT" \
    --format json
done

Notes:

• The --voice value is the lowercase Coval catalog name (vidya, marshal, ziyu, cletus, kehinde, darryl), not the display label.
• Use the same --prompt, the same --wait-seconds, and the shared en language for every accent persona so the accent voice is the only thing that changes between runs. Do not pass Standard Customer's en-US to an accent voice — most accent voices reject it (see the language note above).
• coval personas create does not expose conversation_initiation or background_sound, so new personas take the defaults. If your Standard Customer overrides either, the accent personas will differ on that secondary setting — note it, or set those fields in the app for a pixel-perfect mirror.
• Do not overwrite an existing persona. If one already exists with a different voice or prompt, tell the user and let them decide — do not silently recreate.

Step 5: Resolve Persona IDs

Resolve all seven persona IDs by exact-name match (Standard Customer plus the six accent personas) into ordered arrays the launch step reuses. Keep Standard Customer first so it is the baseline column in the report:

# Canonical persona order — Standard Customer first (baseline).
PERSONA_NAMES=(
  "Standard Customer"
  "Indian Accent (Vidya)"
  "German Accent (Marshal)"
  "Chinese Accent (Ziyu)"
  "US Southern Accent (Cletus)"
  "Nigerian Accent (Kehinde)"
  "Malaysian Accent (Darryl)"
)

PERSONA_IDS=()
for name in "${PERSONA_NAMES[@]}"; do
  id=$(coval personas list --filter "name:\"$name\"" --format json \
    | jq -r --arg n "$name" '.[] | select(.name == $n) | .id' | head -1)
  if [ -z "$id" ]; then
    echo "MISSING persona: $name — resolve Step 4 before launching" >&2
    exit 1
  fi
  PERSONA_IDS+=("$id")
  echo "$id  $name"
done

If any persona is still missing after Step 4, the loop stops — fix it before launching. Do not skip an accent.

Step 6: Launch The Runs

Launch seven runs — one per persona — with the same agent, test set, metrics, and (if subsampling) the same --sub-sample-seed. Keep --concurrency low because these are lower-concurrency voices. Use shared tags so the runs are easy to find later.

SUB_SAMPLE_SEED=42
SUB_SAMPLE_SIZE=0   # 0 = all cases; set to a positive integer to subsample
CONCURRENCY=2       # low on purpose — accent voices are lower-concurrency
TAGS="accent-testing,cookbook"
metric_args=()
if [ -n "${METRIC_IDS:-}" ]; then
  metric_args=(--metric-ids "$METRIC_IDS")
fi

# Uses PERSONA_NAMES / PERSONA_IDS from Step 5; captures each run_id into RUN_IDS.
RUN_IDS=()
for i in "${!PERSONA_IDS[@]}"; do
  persona_id="${PERSONA_IDS[$i]}"
  persona_name="${PERSONA_NAMES[$i]}"
  resp=$(coval runs launch \
    --agent-id "$AGENT_ID" \
    --persona-id "$persona_id" \
    --test-set-id "$TEST_SET_ID" \
    "${metric_args[@]}" \
    --concurrency "$CONCURRENCY" \
    --sub-sample-size "$SUB_SAMPLE_SIZE" \
    --sub-sample-seed "$SUB_SAMPLE_SEED" \
    --tags "$TAGS" \
    --name "Accent — $persona_name" \
    --format json)
  run_id=$(echo "$resp" | jq -r '.run_id // .id')
  RUN_IDS+=("$run_id")
  echo "launched $persona_name -> $run_id"
done

The loop appends each launched run_id to RUN_IDS, which Step 7 watches and Step 8 turns into the report URL.

Tip: the public API also supports a batch launch (POST /v1/runs/batch) if you prefer one HTTP call. The CLI does not expose batch launch today.

Step 7: Watch For Completion

for run_id in "${RUN_IDS[@]}"; do
  coval runs watch "$run_id"
done

coval runs watch blocks until the run reaches a terminal status (COMPLETED, FAILED, or CANCELLED). Because the accent voices are lower-concurrency, simulations may queue and the sweep may take longer than a standard run — that is expected. Run these in parallel shells or background jobs if you want all seven watched at once.

If any run finishes as FAILED with total_test_cases=0, the run-setup worker crashed before sub-sampling. Re-launch that single run with --sub-sample-size 1 (or omit subsampling) and report the failure as a Coval issue — this has been observed in some orgs.

Step 8: Build The Persona Comparison Table

The report URL in Step 9 opens Coval's report builder, but the grouping is a manual UI step, so it does not by itself hand the user a comparison. The user asked for a report comparing the personas — produce that comparison here, directly from the API, so they get a ready-to-read artifact without touching the app. For every run, list its simulations, pull each simulation's metric values, and aggregate per persona:

# Map metric_id -> name once (include built-ins like Transcription Error).
coval metrics list --include-builtin --page-size 100 --format json \
  | jq -r '.[] | "\(.id)\t\(.metric_name // .name)"' > /tmp/accent_metric_names.tsv

# Gather every simulation's metric values across all runs.
: > /tmp/accent_metrics.jsonl
for i in "${!RUN_IDS[@]}"; do
  run_id="${RUN_IDS[$i]}"
  persona="${PERSONA_NAMES[$i]}"
  coval simulations list --run-id "$run_id" --format json \
    | jq -r '.[].simulation_id' \
    | while read -r sid; do
        coval simulations metrics "$sid" --format json \
          | jq -c --arg p "$persona" --arg s "$sid" \
              '.[] | {persona:$p, sim:$s, metric_id:.metric_id, status:.status, value:.value}' \
          >> /tmp/accent_metrics.jsonl
      done
done

Then aggregate per persona per metric and print one Markdown table the user can read directly — pass rate (YES / total) for binary judges, mean for numeric metrics — ordered Standard Customer first and leading with the recognition metrics (Transcription Error, then STT Word Error Rate only if it actually scored), then task-outcome judges, then call-shape metrics:

Metric (direction)	Standard	Indian	German	Chinese	Southern	Nigerian	Malaysian
Transcription Error (lower=better)	0.04	0.03	0.02	0.03	0.04	0.03	0.03
Conversation Success (higher=better)	100%	80%	80%	100%	100%	80%	80%
…

Rules for the table:

• Mark any metric that is FAILED/missing on most simulations as a measurement gap, not a result — flag it and exclude it from the regression call-outs. STT Word Error Rate is commonly all-FAILED when the agent emits no STT trace spans (see Step 3); say so rather than reporting it as 0 or as an accent signal.
• Call out the largest per-accent deltas vs Standard Customer, but do not over-read thin samples — with a small test set each accent run has few simulations, so treat single-case swings as hypotheses to confirm in Step 10, not conclusions.
• Before blaming the accent, check whether Standard Customer regressed on the same metric/case too. A failure the neutral baseline also hits is an agent, tool, or judge issue, not an accent issue.

Step 9: Create The Saved Report (grouped by Persona)

Create the saved multi-run report directly through the public API so it lands in the org's Reports section already grouped by Persona — no manual builder step. The CLI has no reports command yet, so call the API directly (a coval reports create command would be a good follow-up). Use the same API key your coval CLI is authenticated with.

# Resolve ORG_SLUG from the agent/test-set app URL the user gave you, e.g.
# https://app.coval.dev/acme-co/agents/AbC123 -> ORG_SLUG=acme-co.
ORG_SLUG="${ORG_SLUG:?set ORG_SLUG to the org slug from the Coval app URL}"

# Build the run-id JSON array from the RUN_IDS bash array (Step 6).
RUN_IDS_JSON=$(printf '%s\n' "${RUN_IDS[@]}" | jq -R . | jq -s -c .)

resp=$(curl -s -X POST "https://api.coval.dev/v1/reports" \
  -H "x-api-key: ${COVAL_API_KEY:?use the API key your coval CLI is authenticated with}" \
  -H "content-type: application/json" \
  -d "$(jq -nc --arg name "Accent sweep — $(date +%F)" --argjson run_ids "$RUN_IDS_JSON" \
        '{name: $name, run_ids: $run_ids, compare_by: "persona"}')")

REPORT_ID=$(echo "$resp" | jq -r '.report.id // empty')
if [ -n "$REPORT_ID" ]; then
  echo "Saved report: https://app.coval.dev/${ORG_SLUG}/reports/${REPORT_ID}"
else
  echo "Report not created (response: $resp)" >&2
fi

compare_by: "persona" persists view_config.compareBy=persona, so the saved report opens already grouped by Persona at https://app.coval.dev/<org>/reports/<id>. The default is PRIVATE; pass "permissions": "PUBLIC" only if the user wants a login-free shareable link (that also marks the included runs public).

Fallback — if POST /v1/reports returns 404 (an older Coval without the reports API), emit the report builder URL and have the user group + save by hand instead:

RUN_IDS_CSV=$(IFS=,; echo "${RUN_IDS[*]}")
echo "https://app.coval.dev/${ORG_SLUG}/reports/new?run_ids=${RUN_IDS_CSV}"
# Open it, set Compare by -> Persona (default is None), then Save.

The Step 8 table already gives the user the persona comparison inline; this saved report is the interactive, shareable version of the same data.

Step 10: Hand Off Analysis

Once the comparison table and report exist, point the user at analyze-accent-report for a structured next-fix write-up. The analysis skill turns the grouped report into prompt, STT/confirmation, routing, or coverage recommendations.

Suggest pasting the saved report URL into:

Use the Coval `analyze-accent-report` skill on this report:
<paste saved report URL>

Output Format

When the skill finishes, return a short, actionable summary:

## Accent Testing — Launch Summary

**Agent:** <display_name> (<id>)
**Test set:** <display_name> (<id>)
**Subsample:** <sub_sample_size> cases (seed <sub_sample_seed>) — or "full test set"
**Metrics:** <comma-separated metric names>
**Personas created this run:** <list, or "none — all already existed">

**Runs:**
| Persona | Voice | Run ID | Status | Run link |
|---|---|---|---|---|
| Standard Customer | (baseline) | … | … | https://app.coval.dev/<org>/runs/<id> |
| Indian Accent (Vidya) | vidya | … | … | … |
| German Accent (Marshal) | marshal | … | … | … |
| Chinese Accent (Ziyu) | ziyu | … | … | … |
| US Southern Accent (Cletus) | cletus | … | … | … |
| Nigerian Accent (Kehinde) | kehinde | … | … | … |
| Malaysian Accent (Darryl) | darryl | … | … | … |

**Persona comparison (from Step 8 — recognition metrics first, baseline first):**
| Metric (direction) | Standard | Indian | German | Chinese | Southern | Nigerian | Malaysian |
|---|---|---|---|---|---|---|---|
| Transcription Error (↓) | … | … | … | … | … | … | … |
| <task-outcome judge> (↑) | … | … | … | … | … | … | … |
| … | | | | | | | |

**Largest deltas vs Standard:** <one line per notable accent delta, or "none — all accents within noise of baseline">. Note any `FAILED`/missing metrics as measurement gaps, and flag small-sample (few-sims-per-accent) conclusions as tentative.

**Saved report (grouped by Persona):**
https://app.coval.dev/<org>/reports/<id> — created via `POST /v1/reports` (`compare_by: persona`); opens already grouped. (Fallback if the reports API was unavailable: the builder URL `…/reports/new?run_ids=<csv>` + a manual Compare-by-Persona save.)

**Next step:** run the `analyze-accent-report` skill on the saved report.

Guardrails

• One agent, one test set, same metrics, same subsample seed across all seven runs. Any divergence breaks the grouped comparison the report depends on.
• Make every accent persona mirror Standard Customer's prompt and wait time, and give them all the same shared en language, so the accent voice is the only variable. Do not copy Standard Customer's en-US onto an accent voice — most accent voices reject it; en is the one code all six accept.
• STT Word Error Rate only scores when the agent emits STT trace spans. If it comes back FAILED/empty across the sweep, report it as a trace-setup gap and lean on Transcription Error — never present an unscored metric as a result.
• Always hand the user the Step 8 persona-comparison table. A bare report-builder URL is not the comparison they asked for; the grouped view is a manual UI step.
• Create accent personas only when an exact-name match is missing. Never create duplicates, and never silently overwrite an existing persona.
• Keep --concurrency low and expect queueing — these are lower-concurrency voices. Slow completion is expected, not a failure.
• Do not change the agent's configuration mid-sweep. If you must, launch a fresh sweep so the comparison stays apples-to-apples.
• Do not use chat-only agents. The cookbook explicitly does not apply.
• Do not skip an accent because the org "doesn't serve" that population. The full pack is what makes the comparison interpretable.
• Do not invent persona, agent, test set, or metric IDs — always resolve them from the user's org.