A farrier stall’s ASR set is English, the intent table mixes languages, and the TTS scripts are written in yet another language. What should the candidate conclude about the three corpora?

When a farrier stall’s ASR set is English, the intent table mixes languages, and the TTS scripts are written in yet another language, the three corpora are not language-aligned for one end-to-end conversational pipeline and must be made consistent. Language mix across arms is not a required proof of multilingual fusion. It is false that only the TTS language matters. Helm redeploy does not automatically translate every corpus into a single language. Exam caveat: end-to-end conversational data needs language consistency across ASR, NLP, and TTS arms. Operational check: pick the kiosk’s operating language(s), translate or recollect mismatched arms, and re-test a full hear–intent–speak turn in one coherent language profile. Harmonize farrier ASR, intent, and TTS languages before end-to-end demos. Municipal operators closing q148 should file evidence that the correct concept was implemented on the civic assets named in the stem, that each rejected alternative fails for a distinct conceptual reason, and that an operational check appears in logs or dataset cards. Keeping that evidence with the run packet prevents silent drift back to distractor thinking during later maintenance. Municipal operators closing q148 should file evidence that the correct concept was implemented on the civic assets named in the stem, that each rejected alternative fails for a distinct conceptual reason, and that an operational check appears in logs or dataset cards. Keeping that evidence with the run packet prevents silent drift back to distractor thinking during later maintenance. Reference q148 in the evidence filename for traceability.