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, wherein the training further comprises the steps of: estimating parameters with new features obtained through a transformed matrix, and using a maximum likelihood formula with new features to perform the training; and g) saving said acoustic model.
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, wherein the training further comprises the steps of: estimating parameters with new features obtained through a transformed matrix, and using a maximum likelihood formula with new features to perform the training; and g) saving said acoustic model. 9. The method of claim 1 , wherein step (c) is performed based on an error rate of tied state classes per an acoustic model.