In this paper, a wave decoder based on the general re-entrant network for continuous speech recognition is described. The decoder design is based on the concept of self-adjusting decoding graph in which the decoding network is expanded and released frame-synchronously. The fast network expansion and release are made possible by utilizing a novel dynamic network scaffolding layer. The self-adjusting decoding graph is obtained by slicing the traditional decoding network horizontally for separation of different knowledge sources and vertically according to each time instant in search. A two layer hashing structure and an admissible arc predication scheme are described. These methods significantly reduce the arc mortality rate, a problem which plagues the efficiency of the dynamic decoder. Experimental results demonstrate that an order of magnitude reduction of decoding resources can be achieved based on the proposed approach.