An Investigation Into Using Parallel Data For Far-Field Speech Recognition

Far-field speech recognition is an important yet challenging task due to low signal to noise ratio. In this paper, three novel deep neural network architectures are explored to improve the far-field speech recognition accuracy by exploiting the parallel far-field and close-talk recordings. All three novel architectures use multi-task learning for the model optimization but focus on three different ideas: dereverberation and recognition joint-learning, close-talk and far-field model knowledge sharing, and environment-code aware training. Experiments on the AMI single distant microphone (SDM) task show that each of the proposed method can boost accuracy individually, and additional improvement can be obtained with appropriate integration of these models. Overall we reduced the error rate by 10% relatively on the SDM set by exploiting the IHM data.