Semantic processing method and device for semantic comprehension model and storage medium

Abstract

The invention provides a semantic processing method for a semantic comprehension model. The semantic processing method comprises the steps of obtaining voice instruction information and noise information of a vehicle-mounted environment; in response to the voice instruction information, converting the voice instruction into corresponding recognizable text information according to the noise information of the vehicle-mounted environment; and triggering a corresponding business process according to an object matched with the hidden variable of the word level and a task field corresponding to thehidden variable of the word level so as to complete a task corresponding to the voice instruction information. The invention further provides a processing device for the language semantic comprehension model and a storage medium. According to the method, the training precision and the training speed of the semantic comprehension model can be improved, so that the semantic comprehension model canadapt to a vehicle-mounted full duplex use scene, the influence of environmental noise on the semantic comprehension model is avoided, and the accuracy of semantic comprehension in a vehicle-mounted full duplex environment is improved.

Description

technical field [0001] The invention relates to machine learning technology, in particular to a semantic processing method, device and storage medium of a semantic understanding model. Background technique [0002] In the usage scenario of full-duplex voice interaction, it is necessary to implement the following operations in a multi-sound source environment where multiple sound sources continuously emit sound at the same time: for example, the recognition of voice identities (male, female, and child), triggering dialogues with different contents, voice Emotion recognition, music / singing recognition, etc.; environmental processing, background noise recognition and echo cancellation, in this process, the semantic understanding model has nothing to do with the field of background noise and other people's chatting in the full-duplex dialogue scene (OOD, Out -Of-Domain) corpus is easier to be listened to by assistants. If such corpus is misresponsed by smart assistants, the inte...

AI Technical Summary

Problems solved by technology

[0002] In the usage scenario of full-duplex voice interaction, it is necessary to implement the following operations in a multi-sound source environment where multiple sound sources continuously emit sound at the same time: for example, the recognition of voice identities (male, female, and child), triggering dialogues with different contents, voice Emotion recognition, music / singing recognition, etc.; environmental processing, background noise recognition and echo cancellation, in this process, the semantic understanding model has nothing to do with the field of background noise and other people's chatting in the full-duplex dialogue scene (OOD, Out -Of-Domain) corpus is easier to be listened to by assistants. If such corpus is misresponsed by smart assistants, the interaction success rate is low and affects the user experience

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