From what I read and understood, when we have a discrete hidden variable that we already know its particular value (instead of summing/marginalizing over them) associated with data then it is appropriate to use the Maximum Likelihood Estimation (MLE). For example, in a labeled corpus when let's say Y is the class to be predicted, and it is a hidden variable at the same time for unlabeled new example, but as we have a labeled corpus for training/estimation so we already know the exact value of Y for each example in the dataset/corpus.
Can we say in a labeled corpus (supervised learning), where the discrete hidden variable values are known, we are supposed to use MLE rather than EM?