In this paper we describe system architectures for robust MLLR based environmental adaptation of continuous speech recognition systems. Inspired by an existing broadcast news transcription system [1] we refined the identification of acoustic scenarios by using a combined GMM/HMM method. Thus environmental adaptation regarding arbitrary acoustic scenarios beyond speaker changes becomes possible. For deploying acoustic adaptation in interactive applications, such as human machine interaction, a time-synchronous adaptation approach is proposed. For different corpora the evaluation of our approaches shows significant improvements in recognition accuracy while satisfying the constraint of time-synchronous processing.