Combining De-noising Auto-encoder and Recurrent Neural Networks in End-to-End Automatic Speech Recognition for Noise Robustness

Abstract

In this paper, we propose an end-to-end noise-robust automatic speech recognition system through deep-learning implementation of de-noising auto-encoders and recurrent neural networks. We use batch normalization and a novel design for the front-end de-noising auto-encoder, which mimics a two-stage prediction of a single-frame clean feature vector from multi-frame noisy feature vectors. For the backend word recognition, we use an end-to-end system based on bidirectional recurrent neural network with long short-term memory cells. The LSTM-BiRNN is trained via connectionist temporal classification criterion. Its performance is compared to a baseline backend based on hidden Markov models and Gaussian mixture models (HMM-GMM). Our experimental results show that the proposed novel front-end de-noising auto-encoder outperforms the best record we can find for the Aurora 2.0 clean-condition training tasks by an absolute improvement of 1.2% (6.0% vs. 7.2%). In addition, the proposed end-to-end back-end architecture is as good as the traditional HMM-GMM back-end recognizer.