18. The method of claim 15 , 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 set 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 set; and g) estimating the one or more scatter matrices.
18. The method of claim 15 , 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 set 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 set; and g) estimating the one or more scatter matrices. 19. The method of claim 18 , wherein the error rate of step (b) is defined as iε(1, 2, . . . , K).