1. A method for training an acoustic model using the maximum likelihood criteria, comprising the steps of: a. performing a forced alignment of speech training data; b. processing the training data and obtaining estimated scatter matrices, wherein said scatter matrices may comprise one or more of a between class scatter matrix and a within-class scatter matrix, from which mean vectors may be estimated; c. biasing the between class scatter matrix and the within-class scatter matrix; d. diagonalizing the between class scatter matrix and the within class scatter matrix and estimating eigen-vectors to produce transformed scatter matrices; e. obtaining new discriminative features using the estimated vectors, wherein said vectors correspond to the highest discrimination in the new space; f. training a new acoustic model based on said new discriminative features; and g. saving said acoustic model.