Minimum classification error factor analysis (MCE-FA) for automatic speech recognition
Modeling acoustic correlation in automatic speech recognition systems is essential when the speech signal is non stationary or corrupted by noise. We present a statistical method for improved acoustic modeling in continuous density hidden Markov models (HMMs). Factor analysis uses a small number of parameters to model the covariance structure of the speech signal. These parameters are estimated by an Expectation-Maximization algorithm, then further adjusted using discriminative minimum classification error training. Experimental results on 1219 New Jersey town names demonstrate that the proposed method produces faster, smaller and more accurate recognition models.