It is well known that differences between training and testing environments seriously affect speech recognition accuracy. Several environment adaptation techniques have been proposed for eliminating environmental difference, and they are effective when the adaptation data in the testing environment is available beforehand. However, testing environments are not always known beforehand. Therefore, a new framework, which uses testing utterances themselves for adaptation, is effective to cope with such variation. In this paper, a method of testing environment adaptation is proposed, which radually learns the features of testing environment as the testing procedure, and does not need to get some testing samples beforehand.