---
name: tai-ch067-voice-and-multimodal-ai-testing
description: 'Apply chapter 67 of Testing AI, Voice and Multimodal AI Testing, as a workflow for evaluating AI and non-deterministic systems. Use for test planning, eval design, quality review, release evidence, examples, or coaching related to voice and multimodal ai testing.'
---

# Voice and Multimodal AI Testing

Skill name: `tai-ch067-voice-and-multimodal-ai-testing`

Based on **Testing AI: Engineering Confidence in AI Systems** by **Jason Arbon**.

## Purpose

Voice and multimodal systems add new failure modes before the model even starts reasoning.

## Use This Workflow

- Identify the AI behavior or release decision being evaluated.
- Define realistic cases, slices, unacceptable outcomes, and evidence needed for confidence.
- Choose measurements that match the risk: rubric scores, samples, intervals, traces, human review, deterministic checks, or production monitors.
- Report uncertainty, severe failures, and decision impact instead of only a pass/fail result.

## Key Guidance

Voice agents and multimodal systems are non-deterministic pipelines. Quality depends on speech
recognition, turn-taking, images, documents, OCR, retrieval, model reasoning, and final output.
For example, a voice agent can fail because ASR misheard the user, the system interrupted too
early, latency made the conversation awkward, or the model answered correctly in text but with
the wrong emotional tone.

## Apply The Approach

Create representative cases, score them with explicit criteria, review severe failures separately, report uncertainty, and connect the evidence to a concrete decision.

## Expert Notes

At expert level, multimodal testing should include modality-specific error attribution, audio
quality slices, OCR accuracy, image grounding, accessibility checks, latency distributions,
human perception scoring, and adversarial cross-modal cases.