Voice and Multimodal AI Testing

What to do

1. Test accents, dialects, code-switching, background noise, interruptions, silence, long pauses, barge-in, pronunciation, speaker changes, low-quality microphones, phone audio, car audio, and room echo.
2. Measure first-token latency, full-response latency, awkward silence, interruption recovery, and how often users abandon the conversation.
3. Test captions, transcripts, alternate text, screen-reader compatibility, visual contrast, non-visual paths for visual tasks, audio-only fallbacks, keyboard access, and whether the system works for users with hearing, speech, vision, cognitive, or motor differences.
4. Do not collapse soft quality into "vibes." Treat it as measurable.
5. Use preference studies, pairwise comparisons, segment-level reporting, task completion, abandonment, correction rate, replay analysis, and human ratings for tone, trust, comfort, clarity, and brand fit.

Evidence to preserve

• Preserve the inputs, versions, configurations, raw outcomes, and results for retrieval, multimodal, voice multimodal needed to reproduce work on Voice and Multimodal AI Testing.
• Report results for retrieval, multimodal, voice multimodal by relevant slice, separate blocker failures from averages, state uncertainty and blind spots, and connect the result to a release decision.