Building DNN Acoustic Models for Large Vocabulary Speech Recognition
TABLE III: Performance comparison of DNNs, deep convolutional neural networks (DCNNs), and deep local untied neural networks (DLUNNs). We evaluate convolutional models with one layer of convolution (DCNN) and two layers of convolution (DCNN2). We compare models trained with fMLLR features and filter bank (FBank) features. Note that a context window of fMLLR features has a temporal dimension but no meaningful frequency dimension whereas FBank features have meaningful time-frequency axes. As an additional control we train a DCNN on features which are randomly permuted to remove meaningful coherence in both the time and frequency axes (FBank-P and fMLLR-P). We report performance on both the Hub5 Eval2000 test set (EV) which contains Switchboard (SWBD) and CallHome (CH) evaluation subsets.
['Model', 'Features', 'Acc(%)', 'SWBD WER', 'CH WER', 'EV WER']
When using filter bank features, DCNNs and DLUNNs both achieve improvements over DNNs. DCNN models narrowly outperform DLUNN models. For locally connected acoustic models it appears that the constraint of tied weights in convolutional models is advantageous as compared to allowing a different set of localized receptive fields to be learned at different time-frequency regions of the input.