In this paper, the specific application of key words Spotting used in the network monitoring is studied, and the keywords spotting is emphasized. The whole monitoring system is divided into two mod-ules： network moni...In this paper, the specific application of key words Spotting used in the network monitoring is studied, and the keywords spotting is emphasized. The whole monitoring system is divided into two mod-ules： network monitoring and keywords spotting. In the part of network monitoring, this paper adopts a method which is based on ARP spoofing technology to monitor the users＇ data, and to obtain the original audio streams. In the part of keywords spotting, the extraction methods of PLP （one of the main characteristic arameters） is studied, and improved feature parameters- PMCC are put forward. Meanwhile, in order to accurately detect syllable, the paper the double-threshold method with variance of frequency band method, and use the latter to carry out endpoint detection. Finally, keywords recognition module is built by HMM, and identification results are contrasted under Matlab environment. From the experiment results, a better solution for the application of key words recognition technology in network monitoring is found.展开更多
基金supported by the Natural Science Foundation of Guangxi Province(No.60961002)
文摘In this paper, the specific application of key words Spotting used in the network monitoring is studied, and the keywords spotting is emphasized. The whole monitoring system is divided into two mod-ules： network monitoring and keywords spotting. In the part of network monitoring, this paper adopts a method which is based on ARP spoofing technology to monitor the users＇ data, and to obtain the original audio streams. In the part of keywords spotting, the extraction methods of PLP （one of the main characteristic arameters） is studied, and improved feature parameters- PMCC are put forward. Meanwhile, in order to accurately detect syllable, the paper the double-threshold method with variance of frequency band method, and use the latter to carry out endpoint detection. Finally, keywords recognition module is built by HMM, and identification results are contrasted under Matlab environment. From the experiment results, a better solution for the application of key words recognition technology in network monitoring is found.
