Batch-normalized joint training for DNN-based distant speech recognition
Table 1: Performance of the proposed joint training approach compared with other competitive DNN-based systems.
['[ITALIC] SystemDataset', 'TIMIT  [ITALIC] Rev', 'WSJ  [ITALIC] Rev', 'WSJ  [ITALIC] Rev+Noise']
In particular, the first line reports the results obtained with a single neural network. The size of the network has been optimized on the development set (4 hidden layers of 1024 neurons for TIMIT, 6 hidden layers of 2048 neurons for WSJ cases). The second line shows the performance obtained when the speech enhancement neural network (4 hidden layers of 2048 neurons for TIMIT, 6 hidden layers of 2048 neurons for WSJ) is trained independently and later coupled with the close-talking DNN of Sec. These results are particularly critical because, especially in adverse acoustic conditions, the speech enhancement model introduces significant distortions that a close-talking DNN trained in the usual ways is not able to cope with. To partially recover such a critical mismatch, one approach is to first train the speech enhancement, then pass all the training features though the speech enhancement DNN, and, lastly, train the speech recognition DNN with the dataset processed by the speech enhancement. The third line shows results obtained with such a matched training approach. The last line reports the performance achieved with the proposed joint training approach.