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@ARTICLE{Vijayasenan_TASLP_2009,
         author = {Vijayasenan, Deepu and Valente, Fabio and Bourlard, Herv{\'{e}}},
       projects = {Idiap, IM2, AMIDA},
          month = {9},
          title = {An Information Theoretic Approach to Speaker Diarization of Meeting Data},
        journal = {IEEE Transactions on Audio Speech and Language Processing},
         volume = {17},
         number = {7},
           year = {2009},
            doi = {10.1109/TASL.2009.2015698},
       crossref = {vijayasenan:rr08-58},
       abstract = {A speaker diarization system based on an information
theoretic framework is described. The problem is formulated
according to the Information Bottleneck (IB) principle. Unlike
other approaches where the distance between speaker segments
is arbitrarily introduced, the IB method seeks the partition that
maximizes the mutual information between observations and
variables relevant for the problem while minimizing the distortion
between observations. This solves the problem of choosing the
distance between speech segments, which becomes the Jensen-
Shannon divergence as it arises from the IB objective function
optimization. We discuss issues related to speaker diarization
using this information theoretic framework such as the criteria
for inferring the number of speakers, the trade-off between
quality and compression achieved by the diarization system, and
the algorithms for optimizing the objective function. Furthermore
we benchmark the proposed system against a state-of-the-art
system on the NIST RT06 (Rich Transcription) data set for
speaker diarization of meetings. The IB based system achieves a
Diarization Error Rate of 23.2\% compared to 23.6\% for the
baseline system. This approach being mainly based on nonparametric
clustering, it runs significantly faster than the baseline
HMM/GMM based system, resulting in faster-than-real-time
diarization.},
            pdf = {https://publications.idiap.ch/attachments/papers/2009/Vijayasenan_TASLP_2009.pdf}
}



crossreferenced publications: 
@TECHREPORT{vijayasenan:rr08-58,
         author = {Vijayasenan, Deepu and Valente, Fabio and Bourlard, Herv{\'{e}}},
       projects = {Idiap},
          title = {An Information Theoretic Approach to Speaker Diarization of Meeting Data},
           type = {Idiap-RR},
         number = {Idiap-RR-58-2008},
           year = {2008},
    institution = {IDIAP},
           note = {Submitted for publication},
       abstract = {A speaker diarization system based on an information theoretic framework is described. The problem is formulated according to the {\em Information Bottleneck} (IB) principle. Unlike other approaches where the distance between speaker segments is arbitrarily introduced, IB method seeks the partition that maximizes the mutual information between observations and variables relevant for the problem while minimizing the distortion between observations. This solves the problem of choosing the distance between speech segments, which becomes the Jensen-Shannon divergence as it arises from the IB objective function optimization. We discuss issues related to speaker diarization using this information theoretic framework such as the criteria for inferring the number of speakers, the trade-off between quality and compression achieved by the diarization system, and the algorithms for optimizing the objective function. Furthermore we benchmark the proposed system against a state-of-the-art system on the NIST RT06 (Rich Transcription) data set for speaker diarization of meeting. The IB based system achieves a Diarization Error Rate of \(23.2\%\) as compared to \(23.6\%\) of the baseline system. This approach being mainly based on non-parametric clustering, it runs significantly faster then the baseline HMM/GMM based system, resulting in faster-then-real-time diarization.},
            pdf = {https://publications.idiap.ch/attachments/reports/2008/vijayasenan-idiap-rr-08-58.pdf},
     postscript = {ftp://ftp.idiap.ch/pub/reports/2008/vijayasenan-idiap-rr-08-58.ps.gz},
ipdmembership={speech},
}