MCU-based isolated appealing words detecting method with AI techniques
Ye, Liang; Li, Yue; Dong, Wenjing; Seppänen, Tapio; Alasaarela, Esko (2019-07-05)
Ye L., Li Y., Dong W., Seppänen T., Alasaarela E. (2019) MCU-Based Isolated Appealing Words Detecting Method with AI Techniques. In: Han S., Ye L., Meng W. (eds) Artificial Intelligence for Communications and Networks. AICON 2019. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 287. Springer, Cham
© ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering 2019. This is a post-peer-review, pre-copyedit version of an article published in Artificial intelligence for communications and networks : First EAI International Conference, AICON 2019, Harbin, China, May 25–26, 2019 Proceedings, Part II. The final authenticated version is available online at: https://doi.org/10.1007/978-3-030-22971-9_26.
Bullying in campus has attracted more and more attention in recent years. By analyzing typical campus bullying events, it can be found that the victims often use the words “help” and some other appealing or begging words, that is to say, by using the artificial intelligence of speech recognition, we can find the occurrence of campus bullying events in time, and take measures to avoid further harm. The main purpose of this study is to help the guardians discover the occurrence of campus bullying in time by real-time monitoring of the keywords of campus bullying, and take corresponding measures in the first time to minimize the harm of campus bullying. On the basis of Sunplus MCU and speech recognition technology, by using the MFCC acoustic features and an efficient DTW classifier, we were able to realize the detection of common vocabulary of campus bullying for the specific human voice. After repeated experiments, and finally combining the voice signal processing functions of Sunplus MCU, the recognition procedure of specific isolated words was completed. On the basis of realizing the isolated word detection of specific human voice, we got an average accuracy of 99% of appealing words for the dedicated speaker and the misrecognition rate of other words and other speakers was very low.
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