Modeling Acoustic Correlations by Factor Analysis

Hidden Markov models (HMMs) for automatic speech recognition rely on high dimensional feature vectors to summarize the short(cid:173) time properties of speech. Correlations between features can arise when the speech signal is non-stationary or corrupted by noise. We investigate how to model these correlations using factor analysis, a statistical method for dimensionality reduction . Factor analysis uses a small number of parameters to model the covariance struc(cid:173) ture of high dimensional data. These parameters are estimated by an Expectation-Maximization (EM) algorithm that can be em(cid:173) bedded in the training procedures for HMMs.