SPEECH RECOGNITION TRAINING

A method and system performs speech recognition training using Hidden Markov Models. Initially, preprocessed speech signals that include a plurality of observations are stored by the system. Initial Hidden Markov Model (HMM) parameters are then assigned. Summations are then calculated using modified equations derived substantially from the following equations, wherein u.ltoreq.v<w: EQU P(X.sub.u.sup.v)=P(x.sub.u.sup.v)P(x.sub.v+1.sup.w) and EQU .OMEGA..sub.ij (x.sub.u.sup.w)=.OMEGA..sub.ij (x.sub.u.sup.v)P(x.sub.v+1.sup.w)+P(x.sub.u.sup.v).OMEGA..sub.ij (x.sub.v+1.sup.w) The calculated summations are then used to perform HMM parameter reestimation. It then determines whether the HMM parameters have converged. If they have, the HMM parameters are then stored. However, if the HMM parameters have not converged, the system again calculates summations, performs HMM parameter reestimation using the summations, and determines whether the parameters have converged. This process is repeated iteratively until the HMM parameters have converged.