Tied Probabilistic Linear Discriminant Analysis for Speech Recognition
TABLE II: WER (%) using 33 and 109 hours Switchboard training data
['System', 'Feature', '33 hours CHM', '33 hours SWB', '109 hours CHM', '109 hours SWB']
The DNN system has six hidden layers, each with 1024 hidden units when using 33 hours of training data. The number of hidden units is increased to be 1200 when the amount of training data is 109 hours. The bottleneck DNN system (BN hybrid) used the same training data and the same kind of feature input — while reducing the size of the fifth hidden layer to be 26. We concatenated the bottleneck and MFCC_0+Δ+ΔΔ coefficients (referred as BN_MFCC), and then used them to retrain our GMM and PLDA systems. We used LDA to reduce the dimensionality of the concatenated features from 65 to be 40 followed by STC to de-correlate the features for GMM and SGMM systems. Without the front-end feature transforms, the PLDA systems were able to achieve comparable or higher recognition accuracy by directly capturing the correlations between MFCCs and bottleneck features in subspaces. Again, the results demonstrate the flexibility of PLDA acoustic models in terms of using input feature vectors of varying dimension.