Unfortunately, there's always a trade-off. You want both quality data for your use case, but you also want lots of data so it generalizes well. Those are confli

Unfortunately, there's always a trade-off. You want both quality data for your use case, but you also want lots of data so it generalizes well. Those are conflicting goals. Fortunately, splitting models into separate accent-specialized variants and helping them out with language model training will often help in case the model doesn't cope well enough with the cognitive dissonance.