Speech recognition method and system based on environmental parameters and group trend data

Abstract

Disclosed are a method and a system for voice recognition based on environmental parameter and group trend data. The method includes the steps of firstly, acquiring user's voice information and environmental parameter and identification information relative to the voice information and identifying the user according to the identification information; secondly, extracting personal voice and behavior habit characteristic data which are stored in a personal voice and behavior habit characteristic libratory while extracting group voice and habit characteristic data, of a similar group, stored in a group voice and habit characteristic libratory, and judging which functional semantic analysis modules are adaptable to the voice information and sending the relative information to the semantic analysis modules to do individual or combined semantic analysis to obtain a semantic analysis result; and thirdly, storing the semantic analysis result and sending the semantic analysis result and the relative information to the personal voice and behavior habit characteristic libratory and the group voice and habit characteristic libratory to classify and file. By the method and the system, accuracy of voice recognition is improved greatly.

Description

technical field [0001] The invention relates to the field of voice recognition, in particular to a voice recognition method and system based on environmental parameters and group trend data. Background technique [0002] With the development of the Internet and digital multimedia content, the interaction between humans and smart devices has become an important research direction. As the most natural and convenient means of human-computer interaction, voice interaction is the basis for many other applications, such as: voice dialing, voice navigation, indoor device control, etc. Its goal is to convert the lexical content of human speech into computer-readable input, such as keystrokes, binary codes, or sequences of characters. However, the accuracy of speech recognition has always been the bottleneck of speech recognition technology. The main reason is that the information expressed by voice only accounts for a small part of human dialogue, and more information needs to be p...

AI Technical Summary

Problems solved by technology

However, the accuracy of speech recognition has always been the bottleneck of speech recognition technology. The main reason is that the information expressed by voice only accounts for a small part of human dialogue, and more information needs to be provided based on experience, vision, touch, and smell. It is necessary to propose a speech recognition method and system that improves speech recognition accuracy based on speech environment and other information

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