A network teaching method and system with speech recognition function

Abstract

The invention provides a network teaching method and system. Double identity verification of face recognition and voice recognition is realized, the voice signal collection accuracy in a teaching process, an oral training process, a test process and an examination process are scored and assessed by a plurality of models, so that the assessment accuracy is improved, the network teaching is more autonomous and effective, and particularly in reading and listening and recitation teaching, the authenticity and effectiveness of learning can be improved by these functions of the system. By adopting the method provided by the invention, the face recognition is combined with the voice recognition, the user identity is checked before an oral test or system login of the user, and the user can be better encouraged to carry out a human voice test in a use process.

Description

Technical field [0001] The invention relates to a network teaching system and system with speech recognition function. Background technique [0002] In recent years, with the popularization of the mobile Internet and the rapid development of network teaching systems, traditional teaching models have gradually been unable to meet the needs of users for multimedia information teaching methods. In the process of online teaching, the real-time synchronous display of the teaching process, the real-time recording of the teaching process, and the fast and effective on-demand playback have attracted more and more attention from users. In the prior art, in order to meet the above needs, many solutions have been proposed in terms of synchronous display and recording: [0003] Existing technology 1 ("Chinese Journal of Image and Graphics", Volume 8, Issue 9, September 2003, Zhang Liping et al., "A screen sharing solution based on screen copying and compression technology") discloses common s...

AI Technical Summary

Problems solved by technology

This is because most of the existing network teaching systems use video streams and manual screenshots by teachers according to their own judgments in the process of transmitting data on the same screen. Concurrent network transmission often has slow speed, unsmooth display, and data freezes, which affect the students' experience in class, and teachers have to consider timely screenshots while lecturing, and sometimes forget to take screenshots and send them to students. Students can't judge whether they have obtained timely screenshots, which affects the teaching effect

[0012] Specifically, the shortcomings include: 1. The recording process is cumbersome, and some require special personnel to record on-site; 2. The video file is huge, and the quality of the courseware needs to be sacrificed for multiple format conversions to reduce the file size; 3. The server storage system requires high requirements; 4. Local recording requires high configuration and performance of the client machine, slow upload speed, and difficult playback; 5. The role of the simulation system consumes high server resources, which cannot meet the large concurrent network recording requirements of the classroom; 6. The performance of streaming media server clusters for on-demand playback High requirements; 7. On-demand playback requires high server bandwidth; 8. On-demand playback requires users to have high bandwidth (the file is too large); 9. When using 2G / 3G / 4G wireless networks to watch, serious freezes, not smooth enough, and costly high traffic

Moreover, this traditional speech-recognition-based voice-to-text conversion of teachers in class or the scoring of oral texts read by users only uses a relatively fixed language model

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