1. A method of generating a noise adaptive acoustic model, comprising: generating a baseline model parameter from large-capacity speech training data including an environment noise using a processor; and receiving the generated baseline model parameter and applying a discriminative adaptation method to generate a migrated acoustic model parameter, wherein the applying the discriminative adaptation method comprises: dividing the received baseline model parameter into a simplified phoneme like unit model parameter and a context-dependent model parameter; applying the divided simplified phoneme like unit model parameter to the predetermined environment adaptive speech data to perform viterbi decoding; and changing the simplified phoneme like unit model parameter and the context-dependent model parameter in response to the viterbi-decoded results.
1. A method of generating a noise adaptive acoustic model, comprising: generating a baseline model parameter from large-capacity speech training data including an environment noise using a processor; and receiving the generated baseline model parameter and applying a discriminative adaptation method to generate a migrated acoustic model parameter, wherein the applying the discriminative adaptation method comprises: dividing the received baseline model parameter into a simplified phoneme like unit model parameter and a context-dependent model parameter; applying the divided simplified phoneme like unit model parameter to the predetermined environment adaptive speech data to perform viterbi decoding; and changing the simplified phoneme like unit model parameter and the context-dependent model parameter in response to the viterbi-decoded results. 6. The method of claim 1 , wherein the simplified phoneme like unit model parameter exists in an upper layer of the context-dependent model parameter.