Grégory Senay

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In this paper, we present the participation of the Computer Science Laboratory of Avignon (LIA) to RepLab 2013 edition. RepLab is an evaluation campaign for Online Reputation Management Systems. LIA has produced a important number of experiments for every tasks of the campaign: filtering, topic priority detection, Polarity for Reputation and topic(More)
Our goal is to automatically identify faces in TV content without pre-defined dictionary of identities. Most of methods are based on identity detection (from OCR and ASR) and require a propagation strategy based on visual clusterings. In TV content, people appear with many variation making the clustering very difficult. In this case, identifying speakers(More)
This paper is dedicated to the use of auxiliary information in order to help a classical acoustic-based speaker identification system in the specific context of TV shows. The underlying assumption is that auxiliary information could help (1) to rerank n-best speaker hypotheses provided by the acoustic-based only speaker identification system, (2) to provide(More)
In this paper, we describe the LIA system proposed for the MediaEval 2013 Spoken Web Search task. This multilanguage task involves searching for an audio content query, in a database, with no training resources available. The participants must then find locations of each given query term within a large database of untranscribed audio files. For this task,(More)
This paper describes the PERCOLATTE participation to MediaEval 2015 task: “Multimodal Person Discovery in Broadcast TV” which requires developing algorithms for unsupervised talking face identification in broadcast news. The proposed approach relies on two identity propagation strategies both based on document chaptering and restricted overlaid names(More)
This paper is concerned with the speaker diarization task in the specific context of the meeting room recordings. Firstly, different technical improvements of an E-HMM based system are proposed and evaluated in the framework of the NIST RT’06S evaluation campaign. Related experiments show an absolute gain of 6.4% overall speaker diarization error rate (DER)(More)
The detection and characterization, in audiovisual documents, of speech utterances where person names are pronounced, is an important cue for spoken content analysis. This paper tackles the problematic of retrieving spoken person names in the 1-Best ASR outputs of broadcast TV shows. Our assumption is that a person name is a latent variable produced by the(More)
This paper presents a semantic confidence measure that aims to predict the relevance of automatic transcripts for a task of Spoken Document Retrieval (SDR). The proposed predicting method relies on the combination of Automatic Speech Recognition (ASR) confidence measure and a Semantic Compacity Index (SCI), that estimates the relevance of the words(More)
The goal of the PERCOL project is to participate to the REPERE multimodal challenge by building a consortium combining different scientific fields (audio, text and video) in order to perform person recognition in video documents. The two main scientific issues addressed by the challenge are firstly multimodal fusion algorithms for automatic person(More)
In this article, we are interested in spoken term detection task, with a particular focus on Person Name (PN) spotting in automatic speech recognition (ASR) system outputs. We propose a two-step method that combines an acoustic matching based on a Phoneme Confusion Network (PCN) with a semantic rescoring based on the Latent Dirichlet Allocation (LDA)(More)