Speech feature denoising and dereverberation via deep autoencoders for noisy reverberant speech recognition

Denoising autoencoders (DAs) have shown success in generating robust features for images, but there has been limited work in applying DAs for speech. In this paper we present a deep denoising autoencoder (DDA) framework that can produce robust speech features for noisy reverberant speech recognition. The DDA is first pre-trained as restricted Boltzmann machines (RBMs) in an unsupervised fashion. Then it is unrolled to autoencoders, and fine-tuned by corresponding clean speech features to learn a nonlinear mapping from noisy to clean features. Acoustic models are re-trained using the reconstructed features from the DDA, and speech recognition is performed. The proposed approach is evaluated on the CHiME-WSJ0 corpus, and shows a 16-25% absolute improvement on the recognition accuracy under various SNRs.