  Acoustic models using probabilistic linear discriminant analysis (PLDA)
capture the correlations within feature vectors using subspaces which do not
vastly expand the model. This allows high dimensional and correlated feature
spaces to be used, without requiring the estimation of multiple high dimension
covariance matrices. In this letter we extend the recently presented PLDA
mixture model for speech recognition through a tied PLDA approach, which is
better able to control the model size to avoid overfitting. We carried out
experiments using the Switchboard corpus, with both mel frequency cepstral
coefficient features and bottleneck feature derived from a deep neural network.
Reductions in word error rate were obtained by using tied PLDA, compared with
the PLDA mixture model, subspace Gaussian mixture models, and deep neural
networks.
