Acknowledgements
Table 5.6: PER(%) obtained for the test set of TIMIT with various CTC RNN architectures.
['[ITALIC] Arch.Batch-norm.', 'False', 'True']
In these experiments, the Li-GRU clearly outperforms the standard GRU, showing the effectiveness of the proposed model even in a end-to-end ASR framework. The improvement is obtained both with and without batch normalization and, similarly to what observed for hybrid systems, the latter technique leads to better performance when coupled with Li-GRU. However, a smaller performance gain is observed when batch normalization is applied to the CTC. This result could also be related to the different choice of the regularizer, as weight noise was used instead of recurrent dropout.