Daniel Moraru

Step-by-step and integrated approaches in broadcast news speaker diarization

This paper summarizes the collaboration of the LIA and CLIPS laboratories on speaker diarization of broadcast news during the spring NIST Rich Transcription 2003 evaluation campaign (NIST-RTO03S). The speaker diarization task consists of segmenting a conversation into homogeneous segments which are then grouped into speaker classes. Two approaches are described and compared for speaker diarization. The first one relies on a classical two-step speaker diarization strategy based on a detection of speaker turns followed by a clustering process, while the second one uses an integrated strategy where both segment boundaries and speaker tying of the segments are extracted simultaneously and challenged during the whole process. These two methods are used to investigate various strategies for the fusion of diarization results. Furthermore, segmentation into acoustic macro-classes is proposed and evaluated as a priori step to speaker diarization. The objective is to take advantage of the a priori acoustic information in the diariza-tion process. Along with enriching the resulting segmentation with information about speaker gender,

The ELISA consortium approaches in broadcast news speaker segmentation during the NIST 2003 rich transcription evaluation

The paper presents the ELISA consortium activities in automatic speaker segmentation, also known as speaker diarization, during the NIST rich transcription (RT), 2003, evaluation. The experiments were conducted on real broadcast news data (HUB4). Two different approaches from the CLIPS and LIA laboratories are presented and different possibilities of combining them are investigated, in the framework of the ELISA consortium. The system submitted as an ELISA primary system obtained the second lowest segmentation error rate compared to the other RT03-participant primary systems. Another ELISA system submitted as a secondary system outperformed the best primary system and obtained the lowest speaker segmentation error rate.

The ELISA consortium approaches in speaker segmentation during the NIST 2002 speaker recognition evaluation

This paper presents the ELISA consortium activities in automatic speaker segmentation during last NIST 2002 evaluation: two different approaches from CLIPS and LIA laboratories are presented and the possibility of combining them either by applying them consecutively, or by fusing the decisions made by each of them, is investigated. Various types of data were available for NIST 2002. The ELISA systems obtained the lower error rates for two corpora: the CLIPS system obtained the best performance on the Meeting data, the LIA system obtained the best performance on the Switchboard data. The combining strategies proposed in this paper allowed us to improve the performance of the best single system on both data types (up to 30 % of error rate reduction).

Video story segmentation with multi-modal features: experiments on TRECvid 2003

This paper describes the first steps of CLIPS/IMAG on the TREC video story segmentation task. We mostly describe the multi-modal features used and their respective performance for the story segmentation task. These features are based on the audio, video and text modalities. The preliminary system, which has the advantage to be relatively free with respect to the use of training data, is also presented in this paper. First experiments on the TRECVID 2003 evaluation set lead to a recall rate of 0.613 and a precision rate of 0.467. We plan to participate to the official TRECVID 2004 story segmentation task with this system