You can create artificial data synthesis to help mismatched data. However, you must be careful with simulating data from small subgroups.

For example, you can add "Car" noise to the recorded human voice, but you should not repeat the same noise pattern on all datapoints, otherwise your model may get overfit to the added noise.

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1- Carry out manual error analysis to try to understand the difference between training and dev/test sets.

2- Make training data more similar or collect more data similar to dev/test sets.

3- Merge a subset of the dev/test set into the training set and use smaller dev/test sets. It's not a good idea to merge a subset of the training set into dev/test sets.

4- *Training-dev set*: a validation set with the same distribution as training set, but not used for training.

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