Voice recognition method and system based on incremental word graph re-scoring

Abstract

The invention discloses a voice recognition method and system based on incremental word graph re-scoring. The method comprises the steps that: a to-be-recognized voice signal is obtained and acousticfeatures are extracted; a likelihood probability corresponding to the acoustic features is calculated by using a trained acoustic model; a decoder constructs a corresponding decoding network, obtainsa word graph of a state level from the decoding network and obtains a word graph of a word level by updating the word graph and determining the word graph; state-level word graphs of remaining decoding networks are determined, and the determined state-level word graphs are combined with the obtained word-level word graphs to generate a decoded word graph; a target word graph is obtained through afinite-state transcriber merging algorithm according to a re-scoring language model obtained through one-time decoded word graph and small corpus training; and an optimal cost path word graph of the target word graph is obtained, then a corresponding word sequence is obtained, and the word sequence is taken as a final recognition result. According to the invention, the calculation amount of determination after the decoding of a common decoder is finished is reduced, and the decoding speed is accelerated; the word error rate of speech recognition in a specific scene is reduced, and the accuracyis improved.

Description

technical field [0001] The invention belongs to the technical field of speech recognition, and in particular relates to a speech recognition method and system based on incremental word graph re-scoring. Background technique [0002] In recent years, with the rapid development of the artificial intelligence industry, speech recognition technology has received more and more attention from academia and industry. As a front-end technology in the field of speech interaction, speech recognition plays a vital role. It is widely used in many human-computer interaction systems, such as intelligent customer service systems, chat robots, personal intelligent assistants, and smart homes. [0003] At present, the traditional speech recognition technology is mainly built based on the HMM-DNN framework. The advantage of such modeling is that a speech recognition system with good accuracy can be obtained through relatively small data training. The decoder is an extremely important compone...

AI Technical Summary

Problems solved by technology

[0004] In the existing method, in order to ensure the recognition accuracy of one-pass decoding, it is necessary to use a larger beam search width, which will make the final word map too large and the recognition speed is still not fast enough

There is a method to replace the one-step decoding method with a larger beam search width. Although the decoding speed is about 2-3 times higher at lower WERs, there may be a large difference between the two-step decoding because the beam value is too small to affect its final result. use

The method of using GPU parallel computing is relatively expensive, and the wide-scale use of this decoder in industrial scenarios is still open to question

Images