Speech identification method and device, computer equipment and storage medium

Abstract

The application belongs to the technical field of the artificial intelligence, and relates to a speech identification method and device, computer equipment, and a storage medium. The method comprisesthe following steps of acquiring to-be-identified speech information; inputting the to-be-identified speech information into a local first word graph model to perform decoding search to obtain a firstsearch result, wherein the first research result comprises a first path and a corresponding first path score, the first word graph model comprises an acoustic model, a pronunciation dictionary and afirst word graph space; inputting the first search result into a local second word and graph model to search so as to obtain a second search result, wherein the second search result comprises a secondpath and a corresponding second path score, the second word and graph model comprises a second word and graph space, and the first word and graph space is the sub-word and graph space of the second word and graph space; and selecting the corresponding second path to output according to the second path score. The search dimension is lowered, the amount of the word and graph search is reduced, andthe search time is shortened, and the speech identification speed is improved.

Description

technical field [0001] The present application relates to the technical field of artificial intelligence, in particular to a voice recognition method, device, computer equipment and storage medium. Background technique [0002] N-Gram is a language model commonly used in large vocabulary continuous speech recognition. For Chinese, we call it Chinese Language Model (CLM, Chinese Language Model). The Chinese language model can realize automatic conversion to Chinese characters by using the collocation information between adjacent words in the context. [0003] The Chinese language model uses the collocation information between adjacent words in the context. When it is necessary to convert continuous pinyin and strokes without spaces, or numbers representing letters or strokes, into Chinese character strings (ie sentences), it can calculate the maximum probability sentence, thereby realizing the automatic conversion to Chinese characters, without the need for users to manually...

AI Technical Summary

Problems solved by technology

[0005] At present, most mainstream speech recognition decoders have adopted a decoding network based on a finite state machine (WFST). This decoding network integrates a language model, a dictionary, and an acoustic shared phonetic character set into a large decoding network. Acoustic decoding dimension to search, a large number of searches

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