Patent ID: 11935517
Assignee: TENCENT TECHNOLOGY (SHENZHEN) COMPANY LIMITED
Field: Computer technology (Electrical engineering)
Classification: CPC G | IPC G

Claim 15:
16. A non-transitory computer-readable storage medium, storing computer-readable instructions for decoding a speech including a current audio frame and a previous audio frame, the computer-readable instructions, when executed by one or more processors of a computer device, causing the computer device to perform a plurality of operations including:
obtaining a first decoding network corresponding to a low-order language model and a second decoding network corresponding to a differential language model, wherein the low-order language model and the differential language model are based on one high-order language model and the differential language model is derived from performing differential calculation on the high-order language model and the low-order language model;
decoding the previous audio frame using the low-order language model in the first decoding network and using the differential language model in the second decoding network to obtain a first token list, each first token in the first token list comprising a state pair associated with the previous audio frame and a decoding score corresponding to the state pair, the state pair including a first state associated with the low-order language model in the first decoding network and a second state associated with the differential language model in the second decoding network;
identifying, among the first token list, a target token having a smallest decoding score;
determining pruning parameters according to the smallest decoding score of the target token and an acoustic vector of the current audio frame when the current audio frame is decoded, the pruning parameters being used for restricting a decoding process of the current audio frame, further including:
determining a first pruning parameter according to the smallest decoding score of the target token;
inputting the acoustic vector into the first decoding network, traversing non-empty input edges in the first decoding network by using a first state of the target token as a start state, and updating a second initial pruning parameter according to a first decoding path formed by each non-empty input edge, to obtain a second pruning parameter; and
rescoring the first decoding path formed by the non-empty input edge in the second decoding network by using a second state of the target token as a start state, and updating a third initial pruning parameter according to a rescoring result, to obtain a third pruning parameter; and

decoding the current audio frame using the low-order language model in the first decoding network and using the differential language model in the second decoding network according to the first token list, the pruning parameters, and the acoustic vector.