Voice control method and system applied to bicycle environment

Abstract

The invention discloses a voice control method and system applied to a bicycle environment. The voice control method comprises the following steps: acquiring a voice message, and preprocessing the voice message; carrying out voice recognition on the voice message, so that the voice message is converted into a recognition result text; preprocessing the recognition result text; judging the subject of the recognition result text, and judging the intention of the recognition result text and extracting entity information; and searching a database according to the subject, the intention and the entity information of the recognition result text, carrying out matching, and executing the actions corresponding to the recognition result text. The method and the system have the beneficial effects that a mixed model of an artificial neural network model and a hidden markov model is used for carrying out voice recognition, and thus the voice recognition effect is good; the subject is judged first, then the situation that the intention belongs to which intention in the intention set corresponding to the subject is judged, so that the intention of a user can be accurately excavated; manual programming of features is not needed, the accuracy is high, the system expansibility / maintainability is strong, and the time consumption is little.

Description

technical field [0001] The invention relates to the technical field of deep learning, in particular to a voice control method and system applied to a bicycle environment. Background technique [0002] At present, the industry mainly completes the recognition and extraction of semantic information based on the method of feature engineering. However, feature engineering requires a large number of manually labeled data sets and the writing of a large number of rules, which is very time-consuming and labor-intensive; some technologies are based on deep learning to complete this task. , although there is no need to manually write features, it requires a large amount of manual labeling data, which is still time-consuming and the accuracy is not high enough. Contents of the invention [0003] The purpose of the present invention is to overcome the above-mentioned technical deficiencies, propose a voice control method and system applied to the bicycle environment, and solve the ab...

AI Technical Summary

Problems solved by technology

[0002] At present, the industry mainly completes the recognition and extraction of semantic information based on the method of feature engineering. However, feature engineering requires a large number of manually labeled data sets and the writing of a large number of rules, which is very time-consuming and labor-intensive; some technologies are based on deep learning to complete this task. , although there is no need to manually write features, it requires a large amount of manual labeling data, which is still time-consuming and the accuracy is not high enough

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