In this paper, a novel two-stage framework is proposed to copy with speech recognition in adverse environment. First, an on-line HMM composition method which compensates HMMs making use of the on-line testing utterances is proposed in the first stage. By using the proposed method, the dynamic change of environmental noise in each utterance can be well handled. In addition, a classifier trained by using a discriminative learning procedure is incorporated in the second stage to enhance system's discrimination capability. Since the recognition and adaptation processes are carried out in the same session in an unsupervised fashion, this proposed two-stage framework is suitable for practical uses.