[rtbruhan00]

Real-time voice translation looked amazing in demos, but in practice it struggled with accents, technical jargon, and context. The demos were clearly done in controlled environments with clear speakers and simple topics.

The reason? Training data bias and the "last mile" problem - demos use ideal conditions while real usage involves messy audio, overlapping speech, and domain-specific vocabulary the models never saw during training.

[kajolshah_bt]

Totally agree — the “demo vs real world” gap is always the messy edge cases: accents, crosstalk, domain terms, and people talking like… people.

Did you end up adding any guardrails (confidence thresholds, “please repeat,” glossary/term injection, or human fallback)? Also curious: were failures mostly ASR or translation/context?