%Aigaion2 BibTeX export from Idiap Publications
%Saturday 21 December 2024 04:13:17 PM

@INPROCEEDINGS{ajmera-rr-03-38b,
         author = {Ajmera, Jitendra and Wooters, Charles},
       projects = {Idiap},
          title = {A Robust Speaker Clustering Algorithm},
      booktitle = {IEEE Automatic Speech Recognition Understanding Workshop},
           year = {2003},
           note = {IDIAP-RR 03-38},
       crossref = {ajmera-rr-03-38},
       abstract = {In this paper, we present a novel speaker segmentation and clustering algorithm. The algorithm automatically performs both speaker segmentation and clustering without any prior knowledge of the identities or the number of speakers. Advantages of this algorithm over other approaches are: no need for training/development data, no threshold adjustment requirements, and robustness to different data conditions. This paper also reports the performance of the algorithm on different datasets released by NIST with different initial conditions and parameter settings. The consistently low speaker diarization error rate clearly indicates the robustness of the algorithm.},
            pdf = {https://publications.idiap.ch/attachments/reports/2003/ajmera2003asru.pdf},
     postscript = {ftp://ftp.idiap.ch/pub/reports/2003/ajmera2003asru.ps},
ipdmembership={speech},
}



crossreferenced publications: 
@TECHREPORT{ajmera-rr-03-38,
         author = {Ajmera, Jitendra and Wooters, Charles},
       projects = {Idiap},
          title = {A Robust Speaker Clustering Algorithm},
           type = {Idiap-RR},
         number = {Idiap-RR-38-2003},
           year = {2003},
    institution = {IDIAP},
           note = {To appear in IEEE ASRU 2003},
       abstract = {In this paper, we present a novel speaker segmentation and clustering algorithm. The algorithm automatically performs both speaker segmentation and clustering without any prior knowledge of the identities or the number of speakers. Advantages of this algorithm over other approaches are: no need for training/development data, no threshold adjustment requirements, and robustness to different data conditions. This paper also reports the performance of the algorithm on different datasets released by NIST with different initial conditions and parameter settings. The consistently low speaker diarization error rate clearly indicates the robustness of the algorithm.},
            pdf = {https://publications.idiap.ch/attachments/reports/2003/rr03-38.pdf},
     postscript = {ftp://ftp.idiap.ch/pub/reports/2003/rr03-38.ps.gz},
ipdmembership={speech},
}