%Aigaion2 BibTeX export from Idiap Publications
%Thursday 21 November 2024 01:08:27 PM

@INPROCEEDINGS{Vijayasenan_ICASSP-2_2009,
         author = {Vijayasenan, Deepu and Valente, Fabio and Bourlard, Herv{\'{e}}},
       projects = {Idiap, AMIDA, IM2},
          month = {4},
          title = {Mutual Information based Channel Selection for Speaker Diarization of Meetings Data},
      booktitle = {Proceedings of International conference on acoustics speech and signal processing},
           year = {2009},
       abstract = {This paper aims at investigating the use of Kullback-Leibler
(KL) divergence based realignment with application to speaker
diarization. The use of KL divergence based realignment operates
directly on the speaker posterior distribution estimates
and is compared with traditional realignment performed using
HMM/GMM system. We hypothesize that using posterior
estimates to re-align speaker boundaries is more robust than
gaussian mixture models in case of multiple feature streams
with different statistical properties. Experiments are run on
the NIST RT06 data. These experiments reveal that in case
of conventional MFCC features the two approaches yields the
same performance while the KL based system outperforms the
HMM/GMM re-alignment in case of combination of multiple
feature streams (MFCC and TDOA).}
}