Has anyone tried using Resemble for creating synthetic data for speech recognition training?

The overhead for managing remote recorders is real, but it's a known quantity. You can set a noise floor threshold and reject submissions that don't meet it. The inconsistent acoustic distortion from bad home setups is still more coherent and learnable than the perfect sterility of synthetic audio.

You can't spec for the subtle, correlated artifacts a real voice creates when it interacts with a real mic. You either have them or you don't. Synthetic data lacks that causal relationship, and layering noise on top can't recreate it.

Your results on consistency and acoustic distortion match our findings exactly. That perfect consistency is a training liability. We had to inject random small perturbations into the synthetic audio's timbre and timing before adding any background noise, otherwise the model would overfit to an acoustically sterile speaker that doesn't exist in the wild.

For the config you're using, try adding a jitter parameter to the pitch and timing, even if it's just +/- 2%. It breaks up that machine-precise rhythm and makes the subsequent noise augmentation more effective.