Abstract
With the continuous breakthrough of various technologies, speech recognition technology has become a research hotspot. It is a way to find out the phenomenon of bullying in time by detecting whether the voice contains campus bullying vocabulary. In practical applications, an infinite network is established through sensors to transmit information, and the occurrence of campus bullying events is prevented in time. This paper studies the theory of support vector machine and its application in speech recognition. In order to identify bullying vocabulary, this paper firstly built a voice library with 250 voice audios, including 125 campus bullying word audios and 125 non-bullying audios. The first sub-frame of the speech signal was used for endpoint detection. Then mode decomposition and Fourier transform were applied. The maximum value of the primary frequency spectrum was extracted as the acoustic feature. Finally, 200 audios in the database were used for training, and 50 audios were used for speech recognition testing. The average recognition accuracy was 94%, indicating that the support vector machine theory showed a good advantage in the case of small samples for speech recognition.
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Acknowledgement
This work was supported by the National Natural Science Foundation of China (61602127), the Basic scientific research project of Heilongjiang Province (KJCXZD201704), the Key Laboratory of Police Wireless Digital Communication, Ministry of Public Security (2018JYWXTX01), and partly by the Harbin research found for technological innovation (2013RFQXJ104) national education and the science program during the twelfth five-year plan (FCB150518). The authors would like to thank all the people who participated in the project.
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© 2019 ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering
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Liu, T., Ye, L., Han, T., Li, Y., Alasaarela, E. (2019). Speech Bullying Vocabulary Recognition Algorithm in Artificial Intelligent Child Protecting System. 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. https://doi.org/10.1007/978-3-030-22971-9_14
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DOI: https://doi.org/10.1007/978-3-030-22971-9_14
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