11. A method for training acoustic models in speech recognition systems, wherein the speech recognition system comprises a neural network, the method comprising the steps of: a. extracting trainable features from an incoming audio signal using the neural network; and b. processing the trainable features into an acoustic model by the speech recognition system, wherein the neural network comprises at least one of: activation functions with parameters, prealigned feature data, and training, wherein the training is performed using a stochastic gradient descent method on a cost function, and wherein the cost function is a linear discriminant analysis cost function.
11. A method for training acoustic models in speech recognition systems, wherein the speech recognition system comprises a neural network, the method comprising the steps of: a. extracting trainable features from an incoming audio signal using the neural network; and b. processing the trainable features into an acoustic model by the speech recognition system, wherein the neural network comprises at least one of: activation functions with parameters, prealigned feature data, and training, wherein the training is performed using a stochastic gradient descent method on a cost function, and wherein the cost function is a linear discriminant analysis cost function. 13. The method of claim 11 , wherein the processing comprises using at least one of a maximum likelihood method and an expectation maximization method.