Tied Probabilistic Linear Discriminant Analysis for Speech Recognition
TABLE I: WER (%) using 33 hours Switchboard training data, with different feature dimensions and different number of active model parameters
['System', 'Feature', 'Feature dim', '#State-dependent parameters', '#State-indepdent parameters', 'CHM', 'SWB', 'Avg']
In this case, there are about 2,400 clustered triphone states in the GMM systems, corresponding to about 30,000 Gaussians. The PLDA and SGMM systems have a similar number of clustered triphone states, and a 400-component background model is used for each. The state vector of SGMMs and latent variables of PLDA are all 40-dimensional. We used 20,000 sub-state vectors and state variables in the SGMM and tied PLDA systems, respectively. These results demonstrate the flexibility of PLDA systems in using different dimensional acoustic features, i.e. the spliced MFCC_0 without any frontend feature transformations. Tied PLDA systems also offer consistently lower WERs than their counterparts based on the PLDA mixture model. Using the same low dimensional features as MFCC_0(±3)+LDA_STC, the tied PLDA system achieved comparable recognition accuracy to SGMMs. This system is better than tied PLDA systems using spliced MFCC_0 of various context windows, which means that removing the non-discriminative dimensions in feature space is still beneficial to tied PLDAs.