The LIA-Eurecom RT'09 speaker diarization system : enhancements in speaker modelling and cluster purification

We propose in this paper to model the session variability using a specific UBM with a limited amount of components and to associate this session variability mod- elling with a

In this paper, we propose to perform speaker diariza- tion within the dialogue scenes of TV series by combining the au- dio and video modalities: speaker diarization is first

Both bottom-up and top-down approaches are generally based on Hidden Markov Models (HMMs) where each state is a Gaussian Mixture Model (GMM) and corresponds to a speaker.

Novel contributions include: (i) a new approach to BK extraction based on multi-resolution spectral analysis; (ii) an approach to speaker change detection using BKs; (iii)

Select 1st Gaussian

Ex- periments carried out on the REPERE phase 1 test set of TV broadcast programs show the effectiveness of the introduced similarity measures and the NAP compensation method,

(ii) the selection approach which determines which data are used to initialise each speaker model, and (iii) the training algorithm used for speaker modelling in the

On the development data, the overall speaker diarization error was reduced from 24.8% for the best setting of the baseline system to 13.2% using BIC clustering, and to 7.1% with