  PLDA is a popular normalization approach for the i-vector model, and it has
delivered state-of-the-art performance in speaker verification. However, PLDA
training requires a large amount of labeled development data, which is highly
expensive in most cases. A possible approach to mitigate the problem is various
unsupervised adaptation methods, which use unlabeled data to adapt the PLDA
scattering matrices to the target domain.
  In this paper, we present a new `local training' approach that utilizes
inaccurate but much cheaper local labels to train the PLDA model. These local
labels discriminate speakers within a single conversion only, and so are much
easier to obtain compared to the normal `global labels'. Our experiments show
that the proposed approach can deliver significant performance improvement,
particularly with limited globally-labeled data.
