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- 中文 을 입력하시려면 zhongwen을 입력하시고 space를누르시면됩니다.
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RISS 인기검색어
- 검색키워드
- 저자 : ( Adla Abdelkader ) (검색결과 2 건)
- 무료
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- 유료
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Noria Taghezout,Abdelkader Adla,Pascale Zarate 보안공학연구지원센터 2009 International Journal of Software Engineering and Vol.3 No.2
Agents are designed to be autonomous problem-solvers, possibly communicating with other agents and users, and are therefore equipped with sufficient cognitive abilities to reason about a domain, make certain types of decisions themselves, and perform the associated actions. In this paper, we propose to integrate agents in a Cooperative Intelligent Decision Support System. The resulting system, called MACIDS is designed to support operators during contingencies. During the contingency, the operators using MACIDS should be able to: gather information about the incident location; access databases related to the incident; activate predictive modeling programs; support analyses of the operator, and monitor the progress of the situation and action execution. In MACIDS the communication support enhances communication and coordination capabilities of participants. A simple scenario is given, to illustrate the feasibility of the proposal.
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GMM-Based Maghreb Dialect Identification System
( Lachachi Nour Eddine ),( Adla Abdelkader ) 한국정보처리학회 2015 Journal of information processing systems Vol.11 No.1
While Modern Standard Arabic is the formal spoken and written language of the Arab world; dialects are the major communication mode for everyday life. Therefore, identifying a speaker’s dialect is critical in the Arabic-speaking world for speech processing tasks, such as automatic speech recognition or identification. In this paper, we examine two approaches that reduce the Universal Background Model (UBM) in the automatic dialect identification system across the five following Arabic Maghreb dialects: Moroccan, Tunisian, and 3 dialects of the western (Oranian), central (Algiersian), and eastern (Constantinian) regions of Algeria. We applied our approaches to the Maghreb dialect detection domain that contains a collection of 10-second utterances and we compared the performance precision gained against the dialect samples from a baseline GMM-UBM system and the ones from our own improved GMM-UBM system that uses a Reduced UBM algorithm. Our experiments show that our approaches significantly improve identification performance over purely acoustic features with an identification rate of 80.49%.
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