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We investigate densely connected convolutional networks (DenseNets) and theirextension with domain adversarial training for noise robust speech recognition.DenseNets are very deep, compact convolutional neural networks which havedemonstrated incredible improvements over the state-of-the-art results incomputer vision. Our experimental results reveal that DenseNets are more robustagainst noise than other neural network based models such as deep feed forwardneural networks and convolutional neural networks. Moreover, domain adversariallearning can further improve the robustness of DenseNets against both, knownand unknown noise conditions.

Investigation of Densely Connected Convolutional Networks with Domain Adversarial Learning for Noise Robust Speech Recognition