Ground Truth

Ground truth is the set of known-correct reference data that AI models are trained on and evaluated against, for example a perfectly accurate transcript used to score a speech recognizer, or cleanly separated stems used to measure a separation model. The quality of ground truth directly limits how good, and how fairly measured, a model can be. Building it often requires careful human labeling.