  Monaural speech dereverberation is a very challenging task because no spatial
cues can be used. When the additive noises exist, this task becomes more
challenging. In this paper, we propose a joint training method for simultaneous
speech denoising and dereverberation using deep embedding features, which is
based on the deep clustering (DC). DC is a state-of-the-art method for speech
separation that includes embedding learning and K-means clustering. As for our
proposed method, it contains two stages: denoising and dereverberation. At the
denoising stage, the DC network is leveraged to extract noise-free deep
embedding features. These embedding features are generated from the anechoic
speech and residual reverberation signals. They can represent the inferred
spectral masking patterns of the desired signals, which are discriminative
features. At the dereverberation stage, instead of using the unsupervised
K-means clustering algorithm, another supervised neural network is utilized to
estimate the anechoic speech from these deep embedding features. Finally, the
denoising stage and dereverberation stage are optimized by the joint training
method. Experimental results show that the proposed method outperforms the WPE
and BLSTM baselines, especially in the low SNR condition.
