Never-ending learning system for on-line speaker diarization

@article{Markov2007NeverendingLS,
  title={Never-ending learning system for on-line speaker diarization},
  author={Konstantin Markov and Satoshi Nakamura},
  journal={2007 IEEE Workshop on Automatic Speech Recognition & Understanding (ASRU)},
  year={2007},
  pages={699-704}
}
In this paper, we describe new high-performance on-line speaker diarization system which works faster than real-time and has very low latency. It consists of several modules including voice activity detection, novel speaker detection, speaker gender and speaker identity classification. All modules share a set of Gaussian mixture models (GMM) representing pause, male and female speakers, and each individual speaker. Initially, there are only three GMMs for pause and two speaker genders, trained… CONTINUE READING
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