In fact, it's not uncommon to take a dataset and deliberately, randomly distort it- for images, things like scaling, rotating, cropping, blurring, altering colo

In fact, it's not uncommon to take a dataset and deliberately, randomly distort it- for images, things like scaling, rotating, cropping, blurring, altering color balance and gamma, flipping, adding random noise... The idea is to make the model resistant to that "bad" input and effectively enlarge your dataset for free- if you have a picture of a cat, you can automatically also get loads of pictures that you know should still be classified as a cat- rotated 15 degrees clockwise, noisy (like in low-light conditions), the tip of its tail is out of frame, the camera's automatic white balance screws up... Also, the robotic samples may be real human voices mangled by LPC- Think a lossy VoIP call.