Call dropout

Call dropout is a technique used during the training of neural networks, particularly for sequence tasks like speech recognition. It involves temporarily "disabling" or "dropping out" certain parts—like specific features or data points—at random during training. This prevents the model from becoming overly dependent on any one feature, thereby reducing overfitting and improving its ability to generalize to new, unseen data. In essence, call dropout encourages the model to learn more robust patterns, much like how practicing with varied questions prepares someone better for different scenarios.