4. The method of claim 1 , wherein step (b) further comprises the steps of: a) performing tied triphone recognition on the training data using a trained model; b) recording a recognition error rate of each triphone tied state using a transcription of the training data; c) representing a segment of audio corresponding to a triphone with a 39 dimensional Mel-frequency cepstral coefficient feature vector and a first order derivative and a second order derivative; d) mapping training data internally to a tied-triphone state; e) forming a super vector with said Mel-frequency cepstral coefficient features; f) performing a forced Viterbi alignment to assign a tied state label to each frame in the training data; and g) estimating at least one of the between class and with-in class scatter matrices.
4. The method of claim 1 , wherein step (b) further comprises the steps of: a) performing tied triphone recognition on the training data using a trained model; b) recording a recognition error rate of each triphone tied state using a transcription of the training data; c) representing a segment of audio corresponding to a triphone with a 39 dimensional Mel-frequency cepstral coefficient feature vector and a first order derivative and a second order derivative; d) mapping training data internally to a tied-triphone state; e) forming a super vector with said Mel-frequency cepstral coefficient features; f) performing a forced Viterbi alignment to assign a tied state label to each frame in the training data; and g) estimating at least one of the between class and with-in class scatter matrices. 6. The method of claim 4 , wherein step (g) further comprises the steps of: a) estimating a mean of the super vector using the tied state labels of the training data by averaging over each tied state class; and b) estimating a global mean vector.