This CMU Machine Learning Text Book is talking about naive bayes.
Of course we must also estimate the priors on Y as well
$π_k = P(Y = y_k)$
The above model summarizes a Gaussian Naive Bayes classifier, which assumes that the data X is generated by a mixture of class-conditional (i.e., dependent on the value of the class variable Y) Gaussians.
Does mixture here mean is a Gaussian mixture model, which is a probabilistic model that assumes all the data points are generated from a mixture of a finite number of Gaussian distributions with unknown parameters?