Incorporating a Generative Front End Layer to Deep Neural Network for Noise Robust Automatic Speech Recognition
It is difficult to apply well formulated model based noise adaptation approaches to Deep Neural Network (DNN) due to the lack of interpretability of the model parameters. In this paper, we propose incorporating a generative front end layer (GFL) which is parameterised by Gaussian Mixture Model (GMM) into the DNN. A GFL can be easily adapted to different noise conditions by applying the model based Vector Taylor Series (VTS) to the underlying GMM. We show that incorporating a GFL to DNN yields 12.1% relative improvement over a baseline multi condition DNN. We also show that the proposed system performs significantly better than the noise aware training method, where the per utterance estimated noise parameters are appended to the acoustic features.
INTERSPEECH