  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 labelled development data, which is highly
expensive in most cases. We present a cheap PLDA training approach, which
assumes that speakers in the same session can be easily separated, and speakers
in different sessions are simply different. This results in `weak labels' which
are not fully accurate but cheap, leading to a weak PLDA training.
  Our experimental results on real-life large-scale telephony customer service
achieves demonstrated that the weak training can offer good performance when
human-labelled data are limited. More interestingly, the weak training can be
employed as a discriminative adaptation approach, which is more efficient than
the prevailing unsupervised method when human-labelled data are insufficient.

Machine Learning;Artificial Intelligence;Computation and Language;Sound