Local Training for PLDA in Speaker Verification
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.
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