Domain Mismatch Modeling of Out-Domain i-Vectors for PLDA Speaker Verification

Md. Hafizur Rahman, Ivan Himawan, David Dean, Sridha Sridharan


The state-of-the-art i-vector based probabilistic linear discriminant analysis (PLDA) trained on non-target (or out-domain) data significantly affects the speaker verification performance due to the domain mismatch between training and evaluation data. To improve the speaker verification performance, sufficient amount of domain mismatch compensated out-domain data must be used to train the PLDA models successfully. In this paper, we propose a domain mismatch modeling (DMM) technique using maximum-a-posteriori (MAP) estimation to model and compensate the domain variability from the out-domain training i-vectors. From our experimental results, we found that the DMM technique can achieve at least a 24% improvement in EER over an out-domain only baseline when speaker labels are available. Further improvement of 3% is obtained when combining DMM with domain-invariant covariance normalization (DICN) approach. The DMM/DICN combined technique is shown to perform better than in-domain PLDA system with only 200 labeled speakers or 2,000 unlabeled i-vectors.


 DOI: 10.21437/Interspeech.2017-668

Cite as: Rahman, M.H., Himawan, I., Dean, D., Sridharan, S. (2017) Domain Mismatch Modeling of Out-Domain i-Vectors for PLDA Speaker Verification. Proc. Interspeech 2017, 1581-1585, DOI: 10.21437/Interspeech.2017-668.


@inproceedings{Rahman2017,
  author={Md. Hafizur Rahman and Ivan Himawan and David Dean and Sridha Sridharan},
  title={Domain Mismatch Modeling of Out-Domain i-Vectors for PLDA Speaker Verification},
  year=2017,
  booktitle={Proc. Interspeech 2017},
  pages={1581--1585},
  doi={10.21437/Interspeech.2017-668},
  url={http://dx.doi.org/10.21437/Interspeech.2017-668}
}