The discriminative optimization of decoding networks is important for minimizing speech recognition error. Recently, several methods have been reported that optimize decoding networks by extending weighted finite state transducer (WFST)-based decoding processes to a linear classification process. In this paper, we model decoding processes by using conditional random fields (CRFs). Since the maximum mutual information (MMI) training technique is straightforwardly applicable for CRF training, several sophisticated training methods proposed as the variants of MMI can be incorporated in our decoding network optimization. This paper adapts the boosted MMI and the differenced MMI methods for decoding network optimization so that state transition errors are minimized in WFST decoding. We evaluated the proposed methods by conducting large-vocabulary continuous speech recognition experiments. We confirmed that the CRF-based framework and transition error minimization are efficient for improving the accuracy of automatic speech recognizers.