Word graph rescoring method and system for deep learning language models

A deep learning and language model technology, applied in natural language data processing, special data processing applications, instruments, etc., can solve problems such as small search space, insufficient modeling ability of N-Gram language model, and limited improvement of speech recognition performance , to achieve the effect of reducing the number of extensions, reducing memory consumption, and reducing the amount of calculation

Active Publication Date: 2018-08-17
AISPEECH CO LTD
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Problems solved by technology

[0004] In the process of implementing the present invention, the inventors found that due to the structure of multi-candidate list re-scoring, there will be a large amount of redundant information leading to repeated calculations, and the search space is small and the scalability is not strong
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  • Word graph rescoring method and system for deep learning language models
  • Word graph rescoring method and system for deep learning language models
  • Word graph rescoring method and system for deep learning language models

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Embodiment Construction

[0026] In order to make the purpose, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments It is a part of embodiments of the present invention, but not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by persons of ordinary skill in the art without creative efforts fall within the protection scope of the present invention.

[0027] In the following, the embodiment of the present application will be introduced first, and then the experimental data will be used to verify the difference between the solution of the present application and the prior art, and what beneficial effects can be achieved.

[0028] Please refer to figure 1 , which shows a flowchart of an embodime...

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Abstract

The invention discloses a word graph rescoring method and system for deep learning language models for intelligent conversation voice platforms and electronic equipment. The method comprises the following steps of: storing an output word on each edge or each node of a word graph; traversing each node and each edge of the word graph in sequence from a starting node of the word graph, recording ki paths from the starting node to the ith node, and connecting the output words on all the edges or all the nodes on each path in series to form ki word sequences; carrying out reduction processing on the ki word sequences t form ji word sequences; and calculating scores of ji word sequences of each ith node by calling a deep learning language model. According to the method, the word graph is used asa rescoring target, so that the problem of small search space is solved; the problem of redundant repeated calculation is solved by using history cache; history clustering, token pruning and clusterpruning are used for decreasing extension of the word graph, accelerating the calculation and decreasing the memory consumption; and the word graph rescoring efficiency is improved by adoption of a node parallel calculation.

Description

technical field [0001] The invention belongs to the technical field of language model re-scoring, and in particular relates to a word graph re-scoring method, system and electronic equipment for a deep learning language model of an intelligent dialogue voice platform. Background technique [0002] The re-scoring technology refers to dividing speech recognition and decoding into multiple stages, and then using a language model with better performance to re-evaluate the candidate intermediate results for the results of the first-pass recognition and decoding, and finally obtain a new decoding result. According to the different intermediate results, it can be divided into multi-candidate list re-scoring (N-best Rescoring) and word graph re-scoring (LatticeRescoring). Among them, multi-candidate list re-scoring uses the language model with better performance to re-estimate the language score of the top N candidate results obtained by decoding the first pass of speech recognition...

Claims

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Application Information

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IPC IPC(8): G06F17/27G06F17/30
CPCG06F16/353G06F16/355G06F40/289
Inventor 俞凯李豪陈哲怀游永彬
Owner AISPEECH CO LTD
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