A network of deep neural networks for distant speech recognition
Table 1: Performance of the proposed network of DNN approach compared with other competitive DNN-based systems (PER% for TIMIT, WER% for WSJ).
['[ITALIC] SystemDataset', 'TIMIT  [ITALIC] rev', 'WSJ  [ITALIC] rev', 'WSJ  [ITALIC] rev+noise']
The first line reports the results obtained with a single neural network. In this case, only the speech recognition labels are used and the DNN is not forced to perform any speech enhancement task. The second line shows the performance obtained when the single DNN is coupled with a traditional multi-task learning, in which a speech enhancement and a speech recognition task are simultaneously considered. This multi-task architecture shares the first half of the hidden layers across the tasks, while the second half of the architecture is task-dependent. This approach aims to discover (within the shared layers) more general and robust features which can be exploited to better solve both correlated tasks. In this case a bigger DNN composed of a cascade of a speech enhancement and a speech recognition DNNs is jointly trained by back-propagating the speech recognition gradient also into the speech enhancement DNN. The last line finally shows the performance achieved with the proposed network of deep neural network approach. To allow a fair comparison, batch normalization is adopted for all the considered systems. For instance, a relative improvement of about 14% over the single DNN baseline is obtained for the WSJ rev+noise case. The network of deep neural networks also outperforms the considered joint training method. This result suggests that the improved cooperation between the networks achieved with our full communication scheme can overtake the standard DSR pipeline based on a partial and unidirectional information flow (which is still considered in the context of joint training approaches).