Acknowledgements
Table 6.2: Analysis of the role played by batch normalization within the proposed joint training framework.
['[ITALIC] DatasetSystem', 'Without Pre-Training  [ITALIC] no-BN', 'Without Pre-Training  [ITALIC] with-BN', 'With Pre-Training  [ITALIC] no-BN', 'With Pre-Training  [ITALIC] with-BN']
the impact of batch normalization on the joint training framework is shown. The first two columns report, respectively, the results obtained with and without batch normalization when no pre-training techniques are employed. The impact of pre-training is studied in the last two columns. The pre-training strategy considered here consists of initializing the two DNNs with the matched training system discussed in the previous section, and performing a fine-tuning phase with a reduced learning rate. For instance, a relative improvement of about 23% is achieved when batch normalization is adopted for the WSJ task in a noisy and reverberant scenario. From the figure it is clear that batch normalization, when applied to the considered deep joint architecture, ensures a faster convergence and a significantly better performance.