Batch-normalized joint training for DNN-based distant speech recognition
Table 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 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 Sec. 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. The key importance of batch normalization is also highlighted in Fig. 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.