Multi-view Frequency LSTM: An Efficient Frontend for Automatic Speech Recognition
Table 3: Results in % relative WER reduction (WERR) after CTC+sMBR training for a selection of FLSTM model topologies compared to the baseline LSTM acoustic model trained with CTC.
['id', 'model', 'ST', 'MT', 'NT', 'nNT', 'Avg.']
From our experiments, we observed that sMBR gave us an additional gains of 8% relative WER improvement over the CTC seed models. The multi-view FLSTM architectures, particularly models 09 and 13, outperform the baseline AM and single-view FLSTM model across all subsets. Compared to FLSTM, the mvFLSTM model (id 13) achieved an additional 3% relative WERR (Avg.) over the best single view FLSTM, and a relative WERR of 7% on the non-native speakers test set. Model 13 has a similar multi-view FLSTM configuration as model 09 and a projection layer of size 512. This reduced the total amount of free parameters by 36% over model 09, but retains the accuracy achieved by the additional front-end modeling of the multi-view FLSTM layers.