  In this letter, we revisit the problem of maximum likelihood estimation (MLE)
of parameters of Gaussian Mixture Model (GMM) and show a new derivation for its
parameters. The new derivation, unlike the classical approach employing the
technique of expectation-maximization (EM), is straightforward and doesn't
invoke any hidden or latent variables and calculation of the conditional
density function. The new derivation is based on the approach of
minorization-maximization and involves finding a tighter lower bound of the
log-likelihood criterion. The update steps of the parameters, obtained via the
new derivation, are same as the update steps obtained via the classical EM
algorithm.
