Evaluate the agent with users who have non-standard speech – e.g., strong regional accents, non-native speakers, speech impairments (stuttering, lisp), or atypical speaking styles. These can significantly impact ASR performance. For instance, without training, many ASR systems have high error rates on certain accents​

. Create a test dataset of such speakers (or synthesize variations using TTS and audio filters) to measure how accuracy drops. If WER or intent detection accuracy for these groups is far worse than for baseline speakers, that’s an important finding. The benchmark might be to reduce that gap over time (through model improvements or specialized handling). For speech impairments, one might measure if the agent can still catch keywords or whether it times out. A tolerant voice agent might allow slower speech or repeat back what it heard for confirmation. These edge tests ensure the AI is inclusive and effective for a broad user base, not just ideal speakers.