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This paper presents an overview of a state-of-the-art text-independent speaker verification system. First, an introduction proposes a modular scheme of the training and test phases of a speaker verification system. Then, the most commonly speech parameteriza-tion used in speaker verification, namely, cepstral analysis, is detailed. Gaussian mixture(More)
12 Abstract 13 This paper summarizes the collaboration of the LIA and CLIPS laboratories on speaker diarization of 14 broadcast news during the spring NIST Rich Transcription 2003 evaluation campaign (NIST-RTÕ03S). The 15 speaker diarization task consists of segmenting a conversation into homogeneous segments which are then 16 grouped into speaker classes.(More)
This paper presents the ALIZE free speaker recognition toolkit. ALIZE is designed and developed in the framework of the ALIZE project, a part of the French research Ministry Technolangue program. The paper focuses on the innovative aspects of ALIZE and illustrates them by some examples. An experimental validation of the toolkit during the NIST 2004 Speaker(More)
This paper presents an iterative process for blind speaker indexing based on a HMM. This process detects and adds speakers one after the other to the evolutive HMM (E-HMM). The use of this HMM approach takes advantage of the different components of AMIRAL automatic speaker recognition system (ASR system: fron-tend processing, learning, loglikelihood ratio(More)
This paper presents the system used by the LIUM to participate in ESTER, the french broadcast news evaluation campaign. This system is based on the CMU Sphinx 3.3 (fast) decoder. Some tools are presented which have been added on different steps of the Sphinx recognition process: segmentation, acoustic model adaptation, word-lattice rescoring. Several(More)
This paper describes recent advances in speaker diarization by incorporating a speaker identification step. This system builds upon the LIMSI baseline data partitioner used in the broadcast news transcription system. This partitioner provides a high cluster purity but has a tendency to split the data from a speaker into several clusters, when there is a(More)
In this paper, we propose a new clustering model for speaker diarization. A major problem with using greedy agglomerative hierarchical clustering for speaker diariza-tion is that they do not guarantee an optimal solution. We propose a new clustering model, by redefining clustering as a problem of Integer Linear Programming (ILP). Thus an ILP solver can be(More)
In this paper, we consider the extraction of speaker identity from audio records of broadcast news without a priori acoustic information about speakers. Using an automatic speech recognition system and an automatic speaker diariza-tion system, we present improvements for a method which allows to extract speaker identities from automatic transcripts and to(More)