Unsupervised Domain Adaptation for I-vector Speaker Recognition

@inproceedings{GarciaRomero2014UnsupervisedDA,
  title={Unsupervised Domain Adaptation for I-vector Speaker Recognition},
  author={Daniel Garcia-Romero and Alan McCree and Stephen Shum and Niko Br{\"u}mmer and Carlos Vaquero},
  year={2014}
}
In this paper, we present a framework for unsupervised domain adaptation of PLDA based i-vector speaker recognition systems. Given an existing out-of-domain PLDA system, we use it to cluster unlabeled in-domain data, and then use this data to adapt the parameters of the PLDA system. We explore two versions of agglomerative hierarchical clustering that use the PLDA system. We also study two automatic ways to determine the number of clusters in the in-domain dataset. The proposed techniques are… CONTINUE READING
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Generative modelling for unsupervised score calibration

2014 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) • 2014
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Supervised domain adaptation for I-vector based speaker recognition

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