A network of deep neural networks for distant speech recognition
Table 3: Performance with the ResNet-inspired architecture.
['[ITALIC] SystemDataset', 'TIMIT  [ITALIC] rev', 'WSJ  [ITALIC] rev', 'WSJ  [ITALIC] rev+noise']
The architecture depicted in Fig. An alternative solution would be to share the parameters across the various speech enhancement and speech recognition DNNs. However, results (not reported here) show no benefits from this approach. Another possible modification is represented by pre-training each DNN before performing the back-propagation through network. Although a more detailed exploration of alternative solutions is under study, in this section we report some preliminary results obtained by evolving the proposed method with an architecture partly-inspired by residual networks (ResNets) ResNets have recently achieved state-of-the-art performance in computer vision and are based on the idea that, instead of learning a distribution directly, we can learn more easily the residual functions with reference to the input layer. In this work, as shown in Fig. , we employ a ResNet-inspired architecture for the speech enhancement DNNs: ^RSEl=^xSEl−1−^xSEl . (1) This choice is motivated by the fact that we progressively expect less variation between the input and the output of the speech enhancement DNN as the levels increase. Directly learning the residual can thus make the training of the higher levels easier. The results achieved with the ResNet inspired architecture are reported in Tab.