Improving Speech Recognition in Reverberation using a Room-aware Deep Neural Network and Multi-task Learning

In this paper, we propose two approaches to improve deep neural network (DNN) acoustic models for speech recognition in reverberant environments. Both methods utilize auxiliary information in training the DNN but differ in the type of information and the manner in which it is used. The first method uses parallel training data for multi-task learning, in which the network is trained to perform both a primary senone classification task and a secondary feature enhancement task using a shared representation. The second method uses a parameterization of the reverberant environment extracted from the observed signal to train a room-aware DNN. Experiments were performed on the single microphone task of the REVERB Challenge corpus. The proposed approach obtained a word error rate of 7.8% on the SimData test set, which is lower than all reported systems using the same training data and evaluation conditions, and 27.5% on the mismatched RealData test set, which is lower than all but two systems.