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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)
This paper describes recent advances in speaker diarization with a multistage segmentation and clustering system, which incorporates a speaker identification step. This system builds upon the baseline audio partitioner used in the LIMSI broadcast news transcription system. The baseline partitioner provides a high cluster purity, but has a tendency to split(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)
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 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 presents the ELISA speaker segmentation approach applied on multiple audio channel meeting recordings in the framework of NIST RT'04s meeting (spring) evaluation campaign. As done for BN data speaker segmentation, the ELISA " meeting " system involves two speaker segmentation systems developed individually by the CLIPS and LIA laboratories. The(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)