A Comprehensive Study of Deep Bidirectional LSTM RNNs for Acoustic Modeling in Speech Recognition
Table 6: Total times until we get to a certain eval10 WER in a certain train epoch. If not specified, it’s a BLSTM.
['time', 'WER [%]', 'ep', 'model size', 'model details']
That is the summed train epoch time until we reach the specific epoch. We show the model with the best WER up to the specific time. We see that in most cases, combinations of different hyperparameters and methods yield the best results. Time downsampling was a simple method to reduce the calculation time with performance as trade-off.