Data-driven clustering for blind feature mapping in speaker verification

Michael Mason, Robbie Vogt, Brendan Baker, Sridha Sridharan

Handset and channel mismatch degrades the performance of automatic speaker recognition systems significantly. This paper enhances the feature mapping technique by proposing an iterative clustering approach to context model generation which offers an improvement in the performance of feature mapping trained on labelled data and offers the potential to train feature mapping in the absence of correctly labelled background data. The performance of the clustered feature mapping models is demonstrated on an expanded version of the NIST 2003 Extended Data Task (EDT) protocol.

doi: 10.21437/Interspeech.2005-668

Cite as: Mason, M., Vogt, R., Baker, B., Sridharan, S. (2005) Data-driven clustering for blind feature mapping in speaker verification. Proc. Interspeech 2005, 3109-3112, doi: 10.21437/Interspeech.2005-668

@inproceedings{mason05_interspeech,
  author={Michael Mason and Robbie Vogt and Brendan Baker and Sridha Sridharan},
  title={{Data-driven clustering for blind feature mapping in speaker verification}},
  year=2005,
  booktitle={Proc. Interspeech 2005},
  pages={3109--3112},
  doi={10.21437/Interspeech.2005-668}
}