Synthesized stereo mapping via deep neural networks for noisy speech recognition

In our previous work, we extend the traditional stereo-based stochastic mapping by relaxing the constraint of stereo-data, which is not practical in real applications, via HMM-based speech synthesis to construct the “clean” channel data for noisy speech recognition. In this paper, we propose to use deep neural networks (DNNs) for stereo mapping compared with the joint Gaussian mixture model (GMM). The experimental results on Aurora3 databases show that our proposed DNN based synthesized stereo mapping can achieve consistently significant improvements of recognition performance over joint GMM based synthesized stereo mapping in the well-matched (WM) condition among four different European languages.