Application of the Keyword Recognition in the Network Monitoring

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.

HarkMan──A Vocabulary-Independent Keyword Spotter for Spontaneous Chinese Speech

in this paper a novel technique adopted in HarkMan is introduced. HarkMan is a keyword-spotter designed to automatically spot the given words of a vocabulary-independent task in unconstrained Chinese telephone speech. The speak- ing manner and the number of keywords are not limited. This paper focuses on the novel technique which addresses acoustic modeling, keyword spotting network, search strategies, robustness, and rejection. The underlying technologies used in HarkMan given in this paper are useful not only for keyword spotting but also for continuous speech recognition. The system has achieved a figure-of-merit value over 90%.

Semantic Model for Voice Controlled Telephone Dialing and Inquiry Systems

A new scheme is presented to detect a large number ofKeywordsin voice controlled switchboard tasks. The new scheme is based on two stages. In the first stage, N best syllable candidates with their corresponding acoustic scores are generated by an acoustic recognizer. In the second stage, a semantic model based parser is applied to determine the optimum keywords by searching through the lattice of N best candidates. The experimental results show that when the spoken input deviates from the predefined syntactic constraints, the parser can also demonstrate high performance. For comparison purposes, the most common way to incorporate the syntactic knowledge of the task directly into the acoustic recognizer in the form of a finite state network is also investigated. Furthermore, to address the sparse data problems, out of domain data in the form of newspaper text are used to obtain a more robust combined semantic model. The experiments show that the combined semantic model can improve the keywords detection rate from 90.07% to 92.91% when 80 ungrammatical sentences which do not conform to the task grammar are used as testing material.