A Comprehensive Study of Deep Bidirectional LSTM RNNs for Acoustic Modeling in Speech Recognition
Table 4: We try different combinations of dropout and L2. 3 layers, hidden size 500, n\scriptsize chunks=40, Adam. WER on eval10.
['dropout', '[ITALIC] L2', 'WER[%]', 'epoch']
Interestingly, the combination of both L2 and dropout gives a big improvement and yields the best result.