For a regional service workflow, the evaluation should represent how the intended users actually speak. Country labels alone cannot describe that variation.
Collect the relevant speech conditions
Identify the languages, task vocabulary and recording environments within scope. Use authorised recordings or appropriately designed test material, with suitable handling of participant information.
Google’s Speech-to-Text language documentation describes supported languages and model capabilities. Listed support does not establish performance on every code-switching pattern.
An illustrative Singapore customer call may combine English with another language and a product name. The evaluation should retain that sequence rather than convert it into a clean monolingual script.
Examine consequential mistakes
Review amounts, dates, names and requested actions separately from general word errors. A readable transcript can still change the meaning of a customer instruction.
Distinguish transcription failure from misunderstanding in the downstream assistant. If the transcript is wrong, prompt changes in the summariser may not address the source problem.
Include a route for uncertainty and correction. The system should not silently convert an unfamiliar expression into a plausible but unsupported action.
Report performance at the right scope
Describe the tested mix and environment. Do not generalise from a small set of speakers to all users in a country.
Include a test where the speaker switches language around a product code or numerical value. Those boundaries may be more consequential than a long stretch of ordinary conversation. Review the intended action as well as transcription, because the system can recognise most words while misunderstanding the one phrase controlling the workflow.
GAT’s SME automation analysis considers practical operating value. For speech workflows, that value depends on understanding the actual conversation, including the language changes that a simplified test removes.
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