Noise Robust Speech Recognition on Aurora4 by Humans and Machines

Abstract

Although great progress has been made in automatic speech recognition (ASR), significant performance degradation still exists in noisy environments. Based on our previous introduced very deep CNNs, this paper further integrates residual learning to evaluate very deep convolutional residual network (VDCRN) in noisy conditions, which shows more powerful robustness. Then, cluster adaptive training (CAT) is developed on the VDCRN to reduce the mismatch between the training and testing in noisy scenarios. Moreover, the advanced future-vector assisted LSTM-RNN LM is proposed to achieve a further gain. All the proposed approaches are evaluated on Aurora4 and show a significant improvement for each technology. The final system achieves 3.09% WER on Aurora4, which is approaching humans' performance on this task. This is a new milestone for noise-robust ASR on this benchmark.