Elie el Khoury

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Automatic speaker verification (ASV) systems are subject to various kinds of malicious attacks. Replay, voice conversion and speech synthesis attacks drastically degrade the performance of a standard ASV system by increasing its false acceptance rates. This issue raised a high level of interest in the speech research community where the possible voice(More)
I4U is a joint entry of nine research Institutes and Universities across 4 continents to NIST SRE 2012. It started with a brief discussion during the Odyssey 2012 workshop in Singapore. An online discussion group was soon set up, providing a discussion platform for different issues surrounding NIST SRE’12. Noisy test segments, uneven multi-session training,(More)
! A_CU-run6: local feature alone – average fusion of 3 SVM classification results for each concept using various feature representation choices. ! A_CU-run5: linear weighted fusion of A_CU-run6 with two grid-based global features (color moment and wavelet texture). ! A_CU-run4: linear weighted fusion of A_CU-run5 with a SVM classification result using(More)
This paper presents the LIUM open-source speaker diarization toolbox, mostly dedicated to broadcast news. This tool includes both Hierarchical Agglomerative Clustering using well-known measures such as BIC and CLR, and the new ILP clustering algorithm using i-vectors. Diarization systems are tested on the French evaluation data from ESTER, ETAPE and REPERE(More)
Automatic face recognition in unconstrained environments is a challenging task. To test current trends in face recognition algorithms, we organized an evaluation on face recognition in mobile environment. This paper presents the results of 8 different participants using two verification metrics. Most submitted algorithms rely on one or more of three types(More)
This paper evaluates the performance of the twelve primary systems submitted to the evaluation on speaker verification in the context of a mobile environment using the MOBIO database. The mobile environment provides a challenging and realistic test-bed for current state-of-the-art speaker verification techniques. Results in terms of equal error rate (EER),(More)
The process of manually labeling data is very expensive and sometimes infeasible due to privacy and security issues. This paper investigates the use of two algorithms for clustering unlabeled training i-vectors. This aims at improving speaker recognition performance by using state-of-the-art supervised techniques in the context of the NIST i-vector Machine(More)
Audio-Visual People Diarization (AVPD) is an original framework that simultaneously improves audio, video, and audiovisual diarization results. Following a literature review of people diarization for both audio and video content and their limitations, which includes our own contributions, we describe a proposed method for associating both audio and video(More)
Content-based people clustering is a crucial step for people indexing within video documents. In this paper, we investigate the use of both face and clothing features. A method of extracting a <i>keyface</i> for each video sequence is proposed. An algorithm based on the average of the <i>N</i>-minimum pair distances between local invariant features is used(More)
In this paper, we describe a new application for multimedia indexing, using a system that monitors the instrumental activities of daily living to assess the cognitive decline caused by dementia. The system is composed of a wearable camera device designed to capture audio and video data of the instrumental activities of a patient, which is leveraged with(More)