Abstract: A speech decoding method is performed by a computer device, the speech including a current audio frame and a previous audio frame. The method includes: obtaining a target token corresponding to a smallest decoding score from a first token list including first tokens obtained by decoding the previous audio frame, each first token including a state pair and a decoding score, the state pair being used for characterizing a correspondence between a first state of the first token in a first decoding network corresponding to a low-order language model and a second state of the first token in a second decoding network corresponding to a differential language model; determining pruning parameters according to the target token and an acoustic vector of the current audio frame when the current audio frame is decoded; and decoding the current audio frame according to the first token list, the pruning parameters, and the acoustic vector.

Abstract: An assembled subway station and a construction method thereof are provided. The assembled subway station includes a plurality of components combined in a ring shape, comprising a bottom plate component, two bottom corner components, two side wall components, a middle plate component, two top corner components, and a top plate component. Connection surfaces of each component are each provided with a concave-convex structure being matched with another concave-convex structure on a relevant connection surface of an adjacent component. Butt joint positions of two adjacent components are filled with sealing structures. The embedded grooves of two adjacent components are oppositely arranged, and connecting members for exerting opposite fastening forces on the two adjacent components are installed in the oppositely arranged embedded grooves.

Abstract: A speech decoding method is performed by a computer device, the speech including a current audio frame and a previous audio frame. The method includes: obtaining a target token corresponding to a smallest decoding score from a first token list including first tokens obtained by decoding the previous audio frame, each first token including a state pair and a decoding score, the state pair being used for characterizing a correspondence between a first state of the first token in a first decoding network corresponding to a low-order language model and a second state of the first token in a second decoding network corresponding to a differential language model; determining pruning parameters according to the target token and an acoustic vector of the current audio frame when the current audio frame is decoded; and decoding the current audio frame according to the first token list, the pruning parameters, and the acoustic vector.