Abstract: Hidden Markov models (HMMs) rely on high-dimensional feature vectors to summarize the short-time properties of speech correlations between features that can arise when the speech signal is non-stationary or corrupted by noise. These correlations are modeled using factor analysis, a statistical method for dimensionality reduction. Factor analysis is used to model acoustic correlation in automatic speech recognition by introducing a small number of parameters to model the covariance structure of a speech signal. The parameters are estimated by an Expectation Maximization (EM) technique that can be embedded in the training procedures for the HMMs, and then further adjusted using Minimum Classification Error (MCE) training, which demonstrates better discrimination and produces more accurate recognition models.