When budgets force tradeoffs, I would not prioritize dialects or accents based on prestige, convenience, or assumptions about what sounds "standard." I would start with the actual use case. The first question is where the speech data will be used, who the end users are, and what kind of failure would be most costly if the system performs poorly. In practice, that means prioritizing the dialects that represent the highest-volume user interactions or the highest-risk communication settings, whether that is customer support, public services, healthcare, or legal environments. A smaller dialect group may still deserve early inclusion if the cost of misunderstanding is high. For me, the rule is simple: prioritize where speech recognition failure would create the biggest business or human consequence, not just where recording is easiest. One prioritization rule I have used is to rank dialect capture using a combination of audience share, operational importance, and error sensitivity. In plain terms, we ask three things: how many people speak it, how often will it appear in the target workflow, and how damaging is it if the system gets it wrong? That approach keeps the decision grounded. For example, if one accent represents a smaller user segment but appears frequently in compliance-heavy or service-critical interactions, it may move ahead of a larger but lower-risk group. That kind of framework helps teams make realistic decisions under budget pressure without pretending that "one neutral accent" can serve everyone equally.
Many engineering teams will tend to collect data based on total population, but this rarely results in the greatest return for the investment. When budget constraints exist, you should not collect data based on the larger user base, but rather on the higher density of transactions. For example, on a recent project, we had two choices of two different regional dialects to collect data on. Instead of looking at total speaker counts for both dialects, we mapped to the points of the highest value on our client's customer actions areas (checkout flow, account recovery, and subscription management) in order to determine the dialect of the area where the largest number of failed user interactions occurred. The speech pattern of the selected dialect had the highest correlation with the highest dollar amount of unsuccessful transactions. By concentrating the data collection budget on the dialect with the most severe business pain, we decreased support churn greatly, while only being required to collect data from the current geographic footprint of the dialect. In this case, data collection is an issue of capital allocation and not an exercise in storage. If you are not prioritizing based on how your model's failures affect your bottom line, then you are effectively collecting noise. If you are developing production-level systems, the constant trade-offs required are a fact of life. It is almost never about having the most data, but about having the required data at the area of highest friction for the user.
When budgets force tradeoffs, I prioritize recording dialects where we can reuse validated respondent panels or measurement instruments so the work yields defensible, comparable insights quickly. I applied this rule when I redirected leftover year-end budget into a short follow-up survey that reused validated questions and sampling. Because we reused those assets, the results produced clear trend and segmentation insights that tied directly to prior spend and decision needs. My rule is simple: pick dialects that plug into existing, validated samples and measures first so limited funds deliver the most actionable results.